-
A Practical Methodology for Reproducible Experimentation: An Application to the Double-Row Facility Layout Problem Evol. Comput. (IF 6.8) Pub Date : 2024-03-01 Raúl Martín-Santamaría, Sergio Cavero, Alberto Herrán, Abraham Duarte, J. Manuel Colmenar
Reproducibility of experiments is a complex task in stochastic methods such as evolutionary algorithms or metaheuristics in general. Many works from the literature give general guidelines to favor reproducibility. However, none of them provide both a practical set of steps or software tools to help in this process. In this article, we propose a practical methodology to favor reproducibility in optimization
-
Using Decomposed Error for Reproducing Implicit Understanding of Algorithms Evol. Comput. (IF 6.8) Pub Date : 2024-03-01 Caitlin A. Owen, Grant Dick, Peter A. Whigham
Reproducibility is important for having confidence in evolutionary machine learning algorithms. Although the focus of reproducibility is usually to recreate an aggregate prediction error score using fixed random seeds, this is not sufficient. Firstly, multiple runs of an algorithm, without a fixed random seed, should ideally return statistically equivalent results. Secondly, it should be confirmed
-
The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond Evol. Comput. (IF 6.8) Pub Date : 2024-03-01 Anna V. Kononova, Diederick Vermetten, Fabio Caraffini, Madalina-A. Mitran, Daniela Zaharie
We argue that results produced by a heuristic optimisation algorithm cannot be considered reproducible unless the algorithm fully specifies what should be done with solutions generated outside the domain, even in the case of simple bound constraints. Currently, in the field of heuristic optimisation, such specification is rarely mentioned or investigated due to the assumed triviality or insignificance
-
Theoretical Analyses of Multiobjective Evolutionary Algorithms on Multimodal Objectives * Evol. Comput. (IF 6.8) Pub Date : 2023-12-01 Weijie Zheng, Benjamin Doerr
Multiobjective evolutionary algorithms are successfully applied in many real-world multiobjective optimization problems. As for many other AI methods, the theoretical understanding of these algorithms is lagging far behind their success in practice. In particular, previous theory work considers mostly easy problems that are composed of unimodal objectives. As a first step towards a deeper understanding
-
A Data Stream Ensemble Assisted Multifactorial Evolutionary Algorithm for Offline Data-Driven Dynamic Optimization Evol. Comput. (IF 6.8) Pub Date : 2023-12-01 Cuie Yang, Jinliang Ding, Yaochu Jin, Tianyou Chai
Existing work on offline data-driven optimization mainly focuses on problems in static environments, and little attention has been paid to problems in dynamic environments. Offline data-driven optimization in dynamic environments is a challenging problem because the distribution of collected data varies over time, requiring surrogate models and optimal solutions tracking with time. This paper proposes
-
Upgrades of Genetic Programming for Data-Driven Modeling of Time Series Evol. Comput. (IF 6.8) Pub Date : 2023-12-01 A. Murari, E. Peluso, L. Spolladore, R. Rossi, M. Gelfusa
In many engineering fields and scientific disciplines, the results of experiments are in the form of time series, which can be quite problematic to interpret and model. Genetic programming tools are quite powerful in extracting knowledge from data. In this work, several upgrades and refinements are proposed and tested to improve the explorative capabilities of symbolic regression (SR) via genetic programming
-
Characterizing Permutation-Based Combinatorial Optimization Problems in Fourier Space Evol. Comput. (IF 6.8) Pub Date : 2023-09-01 Anne Elorza, Leticia Hernando, Jose A. Lozano
Comparing combinatorial optimization problems is a difficult task. They are defined using different criteria and terms: weights, flows, distances, etc. In spite of this apparent discrepancy, on many occasions, they tend to produce problem instances with similar properties. One avenue to compare different problems is to project them onto the same space, in order to have homogeneous representations.
-
Contributions to Dynamic Analysis of Differential Evolution Algorithms Evol. Comput. (IF 6.8) Pub Date : 2023-09-01 Lucas Resende, Ricardo H. C. Takahashi
The Differential Evolution (DE) algorithm is one of the most successful evolutionary computation techniques. However, its structure is not trivially translatable in terms of mathematical transformations that describe its population dynamics. In this work, analytical expressions are developed for the probability of enhancement of individuals after each application of a mutation operator followed by
-
Symmetry Breaking for Voting Mechanisms * Evol. Comput. (IF 6.8) Pub Date : 2023-09-01 Preethi Sankineni, Andrew M. Sutton
Recently, Rowe and Aishwaryaprajna (2019) introduced a simple majority vote technique that efficiently solves Jump with large gaps, OneMax with large noise, and any monotone function with a polynomial-size image. In this paper, we identify a pathological condition for this algorithm: the presence of spin-flip symmetry in the problem instance. Spin-flip symmetry is the invariance of a pseudo-Boolean
-
Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. Evol. Comput. (IF 6.8) Pub Date : 2023-07-21 Raphael Patrick Prager,Heike Trautmann
The herein proposed Python package pflacco provides a set of numerical features to characterize single-objective continuous and constrained optimization problems. Thereby, pflacco addresses two major challenges in the area optimization. Firstly, it provides the means to develop an understanding of a given problem instance, which is crucial for designing, selecting, or configuring optimization algorithms
-
A Tri-objective Method for Bi-objective Feature Selection in Classification. Evol. Comput. (IF 6.8) Pub Date : 2023-07-18 Ruwang Jiao,Bing Xue,Mengjie Zhang
Minimizing the number of selected features and maximizing the classification performance are two main objectives in feature selection, which can be formulated as a biobjective optimization problem. Due to the complex interactions between features, a solution (i.e., feature subset) with poor objective values does not mean that all the features it selects are useless, as some of them combined with other
-
Preliminary Analysis of Simple Novelty Search. Evol. Comput. (IF 6.8) Pub Date : 2023-07-18 R Paul Wiegand
Novelty search is a powerful tool for finding diverse sets of objects in complicated spaces. Recent experiments on simplified versions of novelty search introduce the idea that novelty search happens at the level of the archive space, rather than individual points. The sparseness measure and archive update criterion create a process that is driven by a two measures: 1) spread out to cover the space
-
Territorial Differential Meta-Evolution: An Algorithm for Seeking All the Desirable Optima of a Multivariable Function. Evol. Comput. (IF 6.8) Pub Date : 2023-06-30 Richard Wehr,Scott R Saleska
Territorial Differential Meta-Evolution (TDME) is an efficient, versatile, and reliable algorithm for seeking all the global or desirable local optima of a multivariable function. It employs a progressive niching mechanism to optimize even challenging, highdimensional functions with multiple global optima and misleading local optima. This article introduces TDME and uses standard and novel benchmark
-
Approaching the Traveling Tournament Problem with Randomized Beam Search. Evol. Comput. (IF 6.8) Pub Date : 2023-09-01 Nikolaus Frohner,Bernhard Neumann,Giulio Pace,Günther R Raidl
The traveling tournament problem is a well-known sports league scheduling problem famous for its practical hardness. Given an even number of teams with symmetric distances between their venues, a double round-robin tournament has to be scheduled minimizing the total travel distances over all teams. We consider the most common constrained variant without repeaters and a streak limit of three, for which
-
Evolutionary Algorithms for Parameter Optimization—Thirty Years Later Evol. Comput. (IF 6.8) Pub Date : 2023-06-01 Thomas H. W. Bäck, Anna V. Kononova, Bas van Stein, Hao Wang, Kirill A. Antonov, Roman T. Kalkreuth, Jacob de Nobel, Diederick Vermetten, Roy de Winter, Furong Ye
Thirty years, 1993–2023, is a huge time frame in science. We address some major developments in the field of evolutionary algorithms, with applications in parameter optimization, over these 30 years. These include the covariance matrix adaptation evolution strategy and some fast-growing fields such as multimodal optimization, surrogate-assisted optimization, multiobjective optimization, and automated
-
A Personal Perspective on Evolutionary Computation: A 35-Year Journey Evol. Comput. (IF 6.8) Pub Date : 2023-06-01 Zbigniew Michalewicz
This paper presents a personal account of the author's 35 years “adventure” with Evolutionary Computation—from the first encounter in 1988 and many years of academic research through to working full-time in business—successfully implementing evolutionary algorithms for some of the world's largest corporations. The paper concludes with some observations and insights.
-
Personal Reflections on Some Early Work in Evolving Strategies in the Iterated Prisoner's Dilemma Evol. Comput. (IF 6.8) Pub Date : 2023-06-01 David B. Fogel
On the occasion of the 30-year anniversary of the Evolutionary Computation journal, I was invited by Professor Hart to offer some reflections on the article on evolving behaviors in the iterated prisoner's dilemma that I contributed to its first issue in 1993. It's an honor to do so. I would like to thank Professor Ken De Jong, the journal's first editor-in-chief, for his vision in creating the journal
-
Comparing Robot Controller Optimization Methods on Evolvable Morphologies. Evol. Comput. (IF 6.8) Pub Date : 2023-05-18 Fuda van Diggelen,Eliseo Ferrante,A E Eiben
In this paper we compare Bayesian Optimization, Differential Evolution, and an Evolution Strategy, employed as a gait learning algorithm in modular robots. The motivational scenario is the joint evolution of morphologies and controllers, where 'newborn' robots also undergo a learning process to optimize their inherited controllers (without changing their bodies). This context raises the question: How
-
Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems. Evol. Comput. (IF 6.8) Pub Date : 2023-12-01 Atanu Mazumdar,Manuel López-Ibáñez,Tinkle Chugh,Jussi Hakanen,Kaisa Miettinen
For offline data-driven multiobjective optimization problems (MOPs), no new data is available during the optimization process. Approximation models (or surrogates) are first built using the provided offline data, and an optimizer, for example, a multiobjective evolutionary algorithm, can then be utilized to find Pareto optimal solutions to the problem with surrogates as objective functions. In contrast
-
Editorial: Reflecting on Thirty Years of ECJ. Evol. Comput. (IF 6.8) Pub Date : 2023-06-01 Kenneth De Jong,Emma Hart
We reflect on 30 years of the journal Evolutionary Computation. Taking the papers published in the first volume in 1993 as a springboard, as the founding and current Editors-in-Chief, we comment on the beginnings of the field, evaluate the extent to which the field has both grown and itself evolved, and provide our own perpectives on where the future lies.
-
Stagnation Detection with Randomized Local Search * Evol. Comput. (IF 6.8) Pub Date : 2023-03-01 Amirhossein Rajabi, Carsten Witt
Recently a mechanism called stagnation detection was proposed that automatically adjusts the mutation rate of evolutionary algorithms when they encounter local optima. The so-called SD-(1++ 1) EA introduced by Rajabi and Witt (2022) adds stagnation detection to the classical (1++ 1) EA with standard bit mutation. This algorithm flips each bit independently with some mutation rate, and stagnation detection
-
Hybridization of Evolutionary Operators with Elitist Iterated Racing for the Simulation Optimization of Traffic Lights Programs Evol. Comput. (IF 6.8) Pub Date : 2023-03-01 Christian Cintrano, Javier Ferrer, Manuel López-Ibáñez, Enrique Alba
In the traffic light scheduling problem, the evaluation of candidate solutions requires the simulation of a process under various (traffic) scenarios. Thus, good solutions should not only achieve good objective function values, but they must be robust (low variance) across all different scenarios. Previous work has shown that combining IRACE with evolutionary operators is effective for this task due
-
An Uncertainty Measure for Prediction of Non-Gaussian Process Surrogates Evol. Comput. (IF 6.8) Pub Date : 2023-03-01 Caie Hu, Sanyou Zeng, Changhe Li
Model management is an essential component in data-driven surrogate-assisted evolutionary optimization. In model management, the solutions with a large degree of uncertainty in approximation play an important role. They can strengthen the exploration ability of algorithms and improve the accuracy of surrogates. However, there is no theoretical method to measure the uncertainty of prediction of Non-Gaussian
-
Evolutionary and Estimation of Distribution Algorithms for Unconstrained, Constrained, and Multiobjective Noisy Combinatorial Optimisation Problems. Evol. Comput. (IF 6.8) Pub Date : 2023-09-01 Aishwaryaprajna,Jonathan E Rowe
We present an empirical study of a range of evolutionary algorithms applied to various noisy combinatorial optimisation problems. There are three sets of experiments. The first looks at several toy problems, such as OneMax and other linear problems. We find that UMDA and the Paired-Crossover Evolutionary Algorithm (PCEA) are the only ones able to cope robustly with noise, within a reasonable fixed
-
Towards Intelligently Designed Evolvable Processors Evol. Comput. (IF 6.8) Pub Date : 2022-12-01 Benedict A. H. Jones, John L. P. Chouard, Bianca C. C. Branco, Eléonore G. B. Vissol-Gaudin, Christopher Pearson, Michael C. Petty, Noura Al Moubayed, Dagou A. Zeze, Chris Groves
Evolution-in-Materio is a computational paradigm in which an algorithm reconfigures a material's properties to achieve a specific computational function. This article addresses the question of how successful and well performing Evolution-in-Materio processors can be designed through the selection of nanomaterials and an evolutionary algorithm for a target application. A physical model of a nanomaterial
-
Active Sets for Explicitly Constrained Evolutionary Optimization Evol. Comput. (IF 6.8) Pub Date : 2022-12-01 Patrick Spettel, Zehao Ba, Dirk V. Arnold
Active-set approaches are commonly used in algorithms for constrained numerical optimization. We propose that active-set techniques can beneficially be employed for evolutionary black-box optimization with explicit constraints and present an active-set evolution strategy. We experimentally evaluate its performance relative to those of several algorithms for constrained optimization and find that the
-
When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization Evol. Comput. (IF 6.8) Pub Date : 2022-12-01 Fernando G. Lobo, Mosab Bazargani
This article investigates the performance of multistart next ascent hillclimbing and well-known evolutionary algorithms incorporating diversity preservation techniques on instances of the multimodal problem generator. This generator induces a class of problems in the bitstring domain which is interesting to study from a theoretical perspective in the context of multimodal optimization, as it is a generalization
-
Uncrowded Hypervolume-Based Multiobjective Optimization with Gene-Pool Optimal Mixing Evol. Comput. (IF 6.8) Pub Date : 2022-09-01 S.C. Maree, T. Alderliesten, P.A.N. Bosman
Domination-based multiobjective (MO) evolutionary algorithms (EAs) are today arguably the most frequently used type of MOEA. These methods, however, stagnate when the majority of the population becomes nondominated, preventing further convergence to the Pareto set. Hypervolume-based MO optimization has shown promising results to overcome this. Direct use of the hypervolume, however, results in no selection
-
Dynastic Potential Crossover Operator Evol. Comput. (IF 6.8) Pub Date : 2022-09-01 Francisco Chicano, Gabriela Ochoa, L. Darrell Whitley, Renato Tinós
An optimal recombination operator for two-parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property). If the solutions are bit strings, the offspring of an optimal recombination operator is optimal in the smallest hyperplane containing the two parent solutions. Exploring this hyperplane is computationally costly
-
On the Construction of Pareto-Compliant Combined Indicators Evol. Comput. (IF 6.8) Pub Date : 2022-09-01 J. G. Falcón-Cardona, M. T. M. Emmerich, C. A. Coello Coello
The most relevant property that a quality indicator (QI) is expected to have is Pareto compliance, which means that every time an approximation set strictly dominates another in a Pareto sense, the indicator must reflect this. The hypervolume indicator and its variants are the only unary QIs known to be Pareto-compliant but there are many commonly used weakly Pareto-compliant indicators such as R2
-
Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multiobjective Evolutionary Algorithm Evol. Comput. (IF 6.8) Pub Date : 2022-06-01 Joost Huizinga, Jeff Clune
An important challenge in reinforcement learning is to solve multimodal problems, where agents have to act in qualitatively different ways depending on the circumstances. Because multimodal problems are often too difficult to solve directly, it is often helpful to define a curriculum, which is an ordered set of subtasks that can serve as the stepping stones for solving the overall problem. Unfortunately
-
Transfer Learning Based Co-Surrogate Assisted Evolutionary Bi-Objective Optimization for Objectives with Non-Uniform Evaluation Times Evol. Comput. (IF 6.8) Pub Date : 2022-06-01 Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer
Most existing multiobjective evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time. Typically. this is untenable in many real-world optimization scenarios where evaluation of different objectives involves different computer simulations or physical experiments with distinct time complexity. To address this issue, a transfer learning
-
Using Well-Understood Single-Objective Functions in Multiobjective Black-Box Optimization Test Suites Evol. Comput. (IF 6.8) Pub Date : 2022-06-01 Dimo Brockhoff, Anne Auger, Nikolaus Hansen, Tea Tušar
Several test function suites are being used for numerical benchmarking of multiobjective optimization algorithms. While they have some desirable properties, such as well-understood Pareto sets and Pareto fronts of various shapes, most of the currently used functions possess characteristics that are arguably underrepresented in real-world problems such as separability, optima located exactly at the
-
Selection Heuristics on Semantic Genetic Programming for Classification Problems Evol. Comput. (IF 6.8) Pub Date : 2022-06-01 Claudia N. Sánchez, Mario Graff
Individual semantics have been used for guiding the learning process of Genetic Programming. Novel genetic operators and different ways of performing parent selection have been proposed with the use of semantics. The latter is the focus of this contribution by proposing three heuristics for parent selection that measure the similarity among individuals' semantics for choosing parents that enhance the
-
Modular Grammatical Evolution for the Generation of Artificial Neural Networks Evol. Comput. (IF 6.8) Pub Date : 2022-06-01 Khabat Soltanian, Ali Ebnenasir, Mohsen Afsharchi
This article presents a novel method, called Modular Grammatical Evolution (MGE), toward validating the hypothesis that restricting the solution space of NeuroEvolution to modular and simple neural networks enables the efficient generation of smaller and more structured neural networks while providing acceptable (and in some cases superior) accuracy on large data sets. MGE also enhances the state-of-the-art
-
VSD-MOEA: A Dominance-Based Multiobjective Evolutionary Algorithm with Explicit Variable Space Diversity Management Evol. Comput. (IF 6.8) Pub Date : 2022-06-01 Joel Chacón Castillo, Carlos Segura, Carlos A. Coello Coello
Most state-of-the-art Multiobjective Evolutionary Algorithms (moeas) promote the preservation of diversity of objective function space but neglect the diversity of decision variable space. The aim of this article is to show that explicitly managing the amount of diversity maintained in the decision variable space is useful to increase the quality of moeas when taking into account metrics of the objective
-
Adaptive Ranking-based Constraint Handling for Explicitly Constrained Black-Box Optimization Evol. Comput. (IF 6.8) Pub Date : 2022-04-05 Naoki Sakamoto,Youhei Akimoto
Abstract We propose a novel constraint-handling technique for the covariance matrix adaptation evolution strategy (CMA-ES). The proposed technique is aimed at solving explicitly constrained black-box continuous optimization problems, in which the explicit constraint is a constraint whereby the computational time for the constraint violation and its (numerical) gradient are negligible compared to that
-
Regret-Based Nash Equilibrium Sorting Genetic Algorithm for Combinatorial Game Theory Problems with Multiple Players. Evol. Comput. (IF 6.8) Pub Date : 2022-09-01 Abdullah Konak,Sadan Kulturel-Konak
We introduce a regret-based fitness assignment strategy for evolutionary algorithms to find Nash equilibria in noncooperative simultaneous combinatorial game theory problems where it is computationally intractable to enumerate all decision options of the players involved in the game. Applications of evolutionary algorithms to non-cooperative simultaneous games have been limited due to challenges in
-
Faster Convergence in Multi-Objective Optimization Algorithms Based on Decomposition Evol. Comput. (IF 6.8) Pub Date : 2022-02-10 Yuri Lavinas,Marcelo Ladeira,Claus Aranha
The Resource Allocation approach (RA) improves the performance of MOEA/D by maintaining a big population and updating few solutions each generation. However, most of the studies on RA generally focused on the properties of different Resource Allocation metrics. Thus, it is still uncertain what the main factors are that lead to increments in performance of MOEA/D with RA. This study investigates the
-
High-Dimensional Unbalanced Binary Classification by Genetic Programming with Multi-Criterion Fitness Evaluation and Selection. Evol. Comput. (IF 6.8) Pub Date : 2022-03-01 Wenbin Pei,Bing Xue,Lin Shang,Mengjie Zhang
High-dimensional unbalanced classification is challenging because of the joint effects of high dimensionality and class imbalance. Genetic programming (GP) has the potential benefits for use in high-dimensional classification due to its built-in capability to select informative features. However, once data are not evenly distributed, GP tends to develop biased classifiers which achieve a high accuracy
-
Multiobjective Evolutionary Algorithms Are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions Evol. Comput. (IF 6.8) Pub Date : 2021-12-01 Chao Qian
As evolutionary algorithms (EAs) are general-purpose optimization algorithms, recent theoretical studies have tried to analyze their performance for solving general problem classes, with the goal of providing a general theoretical explanation of the behavior of EAs. Particularly, a simple multiobjective EA, that is, GSEMO, has been shown to be able to achieve good polynomial-time approximation guarantees
-
A Self-Adaptive Response Strategy for Dynamic Multiobjective Evolutionary Optimization Based on Objective Space Decomposition Evol. Comput. (IF 6.8) Pub Date : 2021-12-01 Ruochen Liu, Jianxia Li, Yaochu Jin, Licheng Jiao
Dynamic multiobjective optimization deals with simultaneous optimization of multiple conflicting objectives that change over time. Several response strategies for dynamic optimization have been proposed, which do not work well for all types of environmental changes. In this article, we propose a new dynamic multiobjective evolutionary algorithm based on objective space decomposition, in which the maxi-min
-
Maximizing Drift Is Not Optimal for Solving OneMax Evol. Comput. (IF 6.8) Pub Date : 2021-12-01 Nathan Buskulic, Carola Doerr
It seems very intuitive that for the maximization of the OneMax problem Om(x):=∑i=1nxi the best that an elitist unary unbiased search algorithm can do is to store a best so far solution, and to modify it with the operator that yields the best possible expected progress in function value. This assumption has been implicitly used in several empirical works. In Doerr et al. (2020), it was formally proven
-
Environmental Adaptation of Robot Morphology and Control Through Real-World Evolution Evol. Comput. (IF 6.8) Pub Date : 2021-12-01 T. F. Nygaard, C. P. Martin, D. Howard, J. Torresen, K. Glette
Robots operating in the real world will experience a range of different environments and tasks. It is essential for the robot to have the ability to adapt to its surroundings to work efficiently in changing conditions. Evolutionary robotics aims to solve this by optimizing both the control and body (morphology) of a robot, allowing adaptation to internal, as well as external factors. Most work in this
-
The Univariate Marginal Distribution Algorithm Copes Well with Deception and Epistasis Evol. Comput. (IF 6.8) Pub Date : 2021-12-01 Benjamin Doerr, Martin S. Krejca
In their recent work, Lehre and Nguyen (2019) show that the univariate marginal distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceptiveLeadingBlocks (DLB) problem. They conclude from this result that univariate EDAs have difficulties with deception and epistasis. In this work, we show that this negative finding is caused by the choice of the parameters
-
Runtime Analysis of Restricted Tournament Selection for Bimodal Optimisation. Evol. Comput. (IF 6.8) Pub Date : 2022-03-01 Edgar Covantes Osuna,Dirk Sudholt
Niching methods have been developed to maintain the population diversity, to investigate many peaks in parallel, and to reduce the effect of genetic drift. We present the first rigorous runtime analyses of restricted tournament selection (RTS), embedded in a (μ+1) EA, and analyse its effectiveness at finding both optima of the bimodal function TwoMax. In RTS, an offspring competes against the closest
-
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions Evol. Comput. (IF 6.8) Pub Date : 2021-09-01 Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, Matt Coler, George Fletcher, Mykola Pechenizkiy
A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from
-
Iterated Local Search and Other Algorithms for Buffered Two-Machine Permutation Flow Shops with Constant Processing Times on One Machine Evol. Comput. (IF 6.8) Pub Date : 2021-09-01 Hoang Thanh Le, Philine Geser, Martin Middendorf
The two-machine permutation flow shop scheduling problem with buffer is studied for the special case that all processing times on one of the two machines are equal to a constant c. This case is interesting because it occurs in various applications, for example, when one machine is a packing machine or when materials have to be transported. Different types of buffers and buffer usage are considered
-
Automatically Evolving Texture Image Descriptors Using the Multitree Representation in Genetic Programming Using Few Instances Evol. Comput. (IF 6.8) Pub Date : 2021-09-01 Harith Al-Sahaf, Ausama Al-Sahaf, Bing Xue, Mengjie Zhang
The performance of image classification is highly dependent on the quality of the extracted features that are used to build a model. Designing such features usually requires prior knowledge of the domain and is often undertaken by a domain expert who, if available, is very costly to employ. Automating the process of designing such features can largely reduce the cost and efforts associated with this
-
Interaction–Transformation Evolutionary Algorithm for Symbolic Regression Evol. Comput. (IF 6.8) Pub Date : 2021-09-01 F. O. de Franca, G. S. I. Aldeia
Interaction–Transformation (IT) is a new representation for Symbolic Regression that reduces the space of solutions to a set of expressions that follow a specific structure. The potential of this representation was illustrated in prior work with the algorithm called SymTree. This algorithm starts with a simple linear model and incrementally introduces new transformed features until a stop criterion
-
An Analysis of the Influence of Noneffective Instructions in Linear Genetic Programming. Evol. Comput. (IF 6.8) Pub Date : 2022-03-01 Léo Françoso Dal Piccol Sotto,Franz Rothlauf,Vinícius Veloso de Melo,Márcio P Basgalupp
Linear Genetic Programming (LGP) represents programs as sequences of instructions and has a Directed Acyclic Graph (DAG) dataflow. The results of instructions are stored in registers that can be used as arguments by other instructions. Instructions that are disconnected from the main part of the program are called noneffective instructions, or structural introns. They also appear in other DAG-based
-
Convergence Analysis of the Hessian Estimation Evolution Strategy Evol. Comput. (IF 6.8) Pub Date : 2021-06-29 Tobias Glasmachers,Oswin Krause
The class of algorithms called Hessian Estimation Evolution Strategies (HE-ESs) update the covariance matrix of their sampling distribution by directly estimating the curvature of the objective function. The approach is practically efficient, as attested by respectable performance on the BBOB testbed, even on rather irregular functions. In this article, we formally prove two strong guarantees for the
-
Shape-constrained Symbolic Regression – Improving Extrapolation with Prior Knowledge Evol. Comput. (IF 6.8) Pub Date : 2021-04-07 G. Kronberger,F. O. de Franca,B. Burlacu,C. Haider,M. Kommenda
We investigate the addition of constraints on the function image and its derivatives for the incorporation of prior knowledge in symbolic regression. The approach is called shape-constrained symbolic regression and allows us to enforce, for example, monotonicity of the function over selected inputs. The aim is to find models which conform to expected behavior and which have improved extrapolation capabilities
-
Errata: Convergence Analysis of Evolutionary Algorithms That Are Based on the Paradigm of Information Geometry Evol. Comput. (IF 6.8) Pub Date : 2020-12-01 Hans-Georg Beyer
Evolutionary Computation, Volume 28, Issue 4, Page 709-710, Winter 2020.
-
Evolved Transistor Array Robot Controllers Evol. Comput. (IF 6.8) Pub Date : 2020-12-01 Michael Garvie, Ittai Flascher, Andrew Philippides, Adrian Thompson, Phil Husbands
For the first time, a field programmable transistor array (FPTA) was used to evolve robot control circuits directly in analog hardware. Controllers were successfully incrementally evolved for a physical robot engaged in a series of visually guided behaviours, including finding a target in a complex environment where the goal was hidden from most locations. Circuits for recognising spoken commands were
-
Evolutionary Image Transition and Painting Using Random Walks Evol. Comput. (IF 6.8) Pub Date : 2020-12-01 Aneta Neumann, Bradley Alexander, Frank Neumann
We present a study demonstrating how random walk algorithms can be used for evolutionary image transition. We design different mutation operators based on uniform and biased random walks and study how their combination with a baseline mutation operator can lead to interesting image transition processes in terms of visual effects and artistic features. Using feature-based analysis we investigate the
-
Inferring Future Landscapes: Sampling the Local Optima Level Evol. Comput. (IF 6.8) Pub Date : 2020-12-01 Sarah L. Thomson, Gabriela Ochoa, Sébastien Verel, Nadarajen Veerapen
Connection patterns among Local Optima Networks (LONs) can inform heuristic design for optimisation. LON research has predominantly required complete enumeration of a fitness landscape, thereby restricting analysis to problems diminutive in size compared to real-life situations. LON sampling algorithms are therefore important. In this article, we study LON construction algorithms for the Quadratic
-
High-Order Entropy-based Population Diversity Measures in the Traveling Salesman Problem Evol. Comput. (IF 6.8) Pub Date : 2020-12-01 Yuichi Nagata
To maintain the population diversity of genetic algorithms (GAs), we are required to employ an appropriate population diversity measure. However, commonly used population diversity measures designed for permutation problems do not consider the dependencies between the variables of the individuals in the population. We propose three types of population diversity measures that address high-order dependencies
-
Genetic Programming Hyper-Heuristics with Vehicle Collaboration for Uncertain Capacitated Arc Routing Problems Evol. Comput. (IF 6.8) Pub Date : 2020-12-01 Jordan MacLachlan, Yi Mei, Juergen Branke, Mengjie Zhang
Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real world than their deterministic counterparts. Although there have been extensive studies in solving routing problems under uncertainty
-
Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis Evol. Comput. (IF 6.8) Pub Date : 2020-12-01 Andrew Lensen, Bing Xue, Mengjie Zhang
Clustering is a difficult and widely studied data mining task, with many varieties of clustering algorithms proposed in the literature. Nearly all algorithms use a similarity measure such as a distance metric (e.g., Euclidean distance) to decide which instances to assign to the same cluster. These similarity measures are generally predefined and cannot be easily tailored to the properties of a particular