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Errata: Convergence Analysis of Evolutionary Algorithms That Are Based on the Paradigm of Information Geometry Evol. Comput. (IF 3.933) Pub Date : 2020-12-01 Hans-Georg Beyer
Evolutionary Computation, Volume 28, Issue 4, Page 709-710, Winter 2020.
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Lower Bounds for Non-Elitist Evolutionary Algorithms via Negative Multiplicative Drift Evol. Comput. (IF 3.933) Pub Date : 2020-11-16 Benjamin Doerr
A decent number of lower bounds for non-elitist population-based evolutionary algorithms has been shown by now. Most of them are technically demanding due to the (hard to avoid) use of negative drift theorems—general results which translate an expected movement away from the target into a high hitting time. We propose a simple negative drift theorem for multiplicative drift scenarios and show that
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A Systematic Literature Review of the Successors of “NeuroEvolution of Augmenting Topologies” Evol. Comput. (IF 3.933) Pub Date : 2020-11-05 Evgenia Papavasileiou; Jan Cornelis; Bart Jansen
NeuroEvolution (NE) refers to a family of methods for optimizing Artificial Neural Networks (ANNs) using Evolutionary Computation (EC) algorithms. NeuroEvolution of Augmenting Topologies (NEAT) is considered one of the most influential algorithms in the field. Eighteen years after its invention, a plethora of methods have been proposed that extend NEAT in different aspects. In this article, we present
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A Decomposition-Based Evolutionary Algorithm with Correlative Selection Mechanism for Many-Objective Optimization Evol. Comput. (IF 3.933) Pub Date : 2020-10-13 Ruochen Liu; Ruinan Wang; Renyu Bian; Jing Liu; Licheng Jiao
Decomposition-based evolutionary algorithms have been quite successful in dealing with multiobjective optimization problems. Recently, more and more researchers attempt to apply the decomposition approach to solve many-objective optimization problems. A many-objective evolutionary algorithm based on decomposition with correlative selection mechanism (MOEA/D-CSM) is also proposed to solve many-objective
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Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming Evol. Comput. (IF 3.933) Pub Date : 2020-10-13 Pak-Kan Wong; Man-Leung Wong; Kwong-Sak Leung
Genetic Programming is a method to automatically create computer programs based on the principles of evolution. The problem of deceptiveness caused by complex dependencies among components of programs is challenging. It is important because it can misguide Genetic Programming to create suboptimal programs. Besides, a minor modification in the programs may lead to a notable change in the program behaviours
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EvoComposer: An Evolutionary Algorithm for 4-Voice Music Compositions. Evol. Comput. (IF 3.933) Pub Date : 2020-09-01 R De Prisco,G Zaccagnino,R Zaccagnino
Evolutionary Computation, Volume 28, Issue 3, Page 489-530, Fall 2020.
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Generating New Space-Filling Test Instances for Continuous Black-Box Optimization. Evol. Comput. (IF 3.933) Pub Date : 2020-09-01 Mario A Muñoz,Kate Smith-Miles
Evolutionary Computation, Volume 28, Issue 3, Page 379-404, Fall 2020.
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Analysis of the (μ/μI,λ)-CSA-ES with Repair by Projection Applied to a Conically Constrained Problem. Evol. Comput. (IF 3.933) Pub Date : 2020-09-01 Patrick Spettel,Hans-Georg Beyer
Evolutionary Computation, Volume 28, Issue 3, Page 463-488, Fall 2020.
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Difficulty Adjustable and Scalable Constrained Multiobjective Test Problem Toolkit. Evol. Comput. (IF 3.933) Pub Date : 2020-09-01 Zhun Fan,Wenji Li,Xinye Cai,Hui Li,Caimin Wei,Qingfu Zhang,Kalyanmoy Deb,Erik Goodman
Evolutionary Computation, Volume 28, Issue 3, Page 339-378, Fall 2020.
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Simple Hyper-Heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes. Evol. Comput. (IF 3.933) Pub Date : 2020-09-01 Andrei Lissovoi,Pietro S Oliveto,John Alasdair Warwicker
Evolutionary Computation, Volume 28, Issue 3, Page 437-461, Fall 2020.
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Diagonal Acceleration for Covariance Matrix Adaptation Evolution Strategies. Evol. Comput. (IF 3.933) Pub Date : 2020-09-01 Y Akimoto,N Hansen
Evolutionary Computation, Volume 28, Issue 3, Page 405-435, Fall 2020.
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Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions. Evol. Comput. (IF 3.933) Pub Date : 2020-06-23 M Virgolin,T Alderliesten,C Witteveen,P A N Bosman
Evolutionary Computation, Ahead of Print.
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Offline Learning with a Selection Hyper-Heuristic: An Application to Water Distribution Network Optimisation. Evol. Comput. (IF 3.933) Pub Date : 2020-06-22 William B Yates,Edward C Keedwell
Evolutionary Computation, Ahead of Print.
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Effect of Objective Normalization and Penalty Parameter on Penalty Boundary Intersection Decomposition-Based Evolutionary Many-Objective Optimization Algorithms. Evol. Comput. (IF 3.933) Pub Date : 2020-06-22 Lei Chen,Kalyanmoy Deb,Hai-Lin Liu,Qingfu Zhang
Evolutionary Computation, Ahead of Print.
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Achieving Highly Scalable Evolutionary Real-Valued Optimization by Exploiting Partial Evaluations. Evol. Comput. (IF 3.933) Pub Date : 2020-06-17 Anton Bouter,Tanja Alderliesten,Peter A N Bosman
Evolutionary Computation, Ahead of Print.
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Feature-Based Diversity Optimization for Problem Instance Classification. Evol. Comput. (IF 3.933) Pub Date : 2020-06-17 Wanru Gao,Samadhi Nallaperuma,Frank Neumann
Evolutionary Computation, Ahead of Print.
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What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-Based Evolutionary Multiobjective Optimisation. Evol. Comput. (IF 3.933) Pub Date : 2020-06-01 Miqing Li,Xin Yao
The quality of solution sets generated by decomposition-based evolutionary multi-objective optimisation (EMO) algorithms depends heavily on the consistency between a given problem's Pareto front shape and the specified weights' distribution. A set of weights distributed uniformly in a simplex often leads to a set of well-distributed solutions on a Pareto front with a simplex-like shape, but may fail
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Multioracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evol. Comput. (IF 3.933) Pub Date : 2020-06-01 Marcel Wever,Lorijn van Rooijen,Heiko Hamann
In software engineering, the imprecise requirements of a user are transformed to a formal requirements specification during the requirements elicitation process. This process is usually guided by requirements engineers interviewing the user. We want to partially automate this first step of the software engineering process in order to enable users to specify a desired software system on their own. With
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Learning and Searching Pseudo-Boolean Surrogate Functions from Small Samples. Evol. Comput. (IF 3.933) Pub Date : 2020-06-01 Kevin Swingler
When searching for input configurations that optimise the output of a system, it can be useful to build a statistical model of the system being optimised. This is done in approaches such as surrogate model-based optimisation, estimation of distribution algorithms, and linkage learning algorithms. This article presents a method for modelling pseudo-Boolean fitness functions using Walsh bases and an
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Automatically Designing State-of-the-Art Multi- and Many-Objective Evolutionary Algorithms. Evol. Comput. (IF 3.933) Pub Date : 2020-06-01 Leonardo C T Bezerra,Manuel López-Ibáñez,Thomas Stützle
A recent comparison of well-established multiobjective evolutionary algorithms (MOEAs) has helped better identify the current state-of-the-art by considering (i) parameter tuning through automatic configuration, (ii) a wide range of different setups, and (iii) various performance metrics. Here, we automatically devise MOEAs with verified state-of-the-art performance for multi- and many-objective continuous
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A Predictive-Reactive Approach with Genetic Programming and Cooperative Coevolution for the Uncertain Capacitated Arc Routing Problem. Evol. Comput. (IF 3.933) Pub Date : 2020-06-01 Yuxin Liu,Yi Mei,Mengjie Zhang,Zili Zhang
The uncertain capacitated arc routing problem is of great significance for its wide applications in the real world. In the uncertain capacitated arc routing problem, variables such as task demands and travel costs are realised in real time. This may cause the predefined solution to become ineffective and/or infeasible. There are two main challenges in solving this problem. One is to obtain a high-quality
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A New Generalized Partition Crossover for the Traveling Salesman Problem: Tunneling between Local Optima. Evol. Comput. (IF 3.933) Pub Date : 2020-06-01 Renato Tinós,Darrell Whitley,Gabriela Ochoa
Generalized Partition Crossover (GPX) is a deterministic recombination operator developed for the Traveling Salesman Problem. Partition crossover operators return the best of 2k reachable offspring, where k is the number of recombining components. This article introduces a new GPX2 operator, which finds more recombining components than GPX or Iterative Partial Transcription (IPT). We also show that
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Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling. Evol. Comput. (IF 3.933) Pub Date : 2020-05-06 Binzi Xu,Yi Mei,Yan Wang,Zhicheng Ji,Mengjie Zhang
Evolutionary Computation, Ahead of Print.
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Evolved Transistor Array Robot Controllers. Evol. Comput. (IF 3.933) Pub Date : 2020-05-01 Michael Garvie,Ittai Flascher,Andrew Philippides,Adrian Thompson,Phil Husbands
Evolutionary Computation, Ahead of Print.
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Evolutionary Image Transition and Painting Using Random Walks. Evol. Comput. (IF 3.933) Pub Date : 2020-02-26 Aneta Neumann,Bradley Alexander,Frank Neumann
Evolutionary Computation, Ahead of Print.
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Inferring Future Landscapes: Sampling the Local Optima Level. Evol. Comput. (IF 3.933) Pub Date : 2020-02-26 Sarah L Thomson,Gabriela Ochoa,Sébastien Verel,Nadarajen Veerapen
Evolutionary Computation, Ahead of Print.
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High-Order Entropy-Based Population Diversity Measures in the Traveling Salesman Problem. Evol. Comput. (IF 3.933) Pub Date : 2020-02-13 Yuichi Nagata
Evolutionary Computation, Ahead of Print.
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Genetic Programming Hyper-Heuristics with Vehicle Collaboration for Uncertain Capacitated Arc Routing Problems. Evol. Comput. (IF 3.933) Pub Date : 2019-11-15 Jordan MacLachlan,Yi Mei,Juergen Branke,Mengjie Zhang
Evolutionary Computation, Ahead of Print.
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Parameterized Analysis of Multiobjective Evolutionary Algorithms and the Weighted Vertex Cover Problem. Evol. Comput. (IF 3.933) Pub Date : 2019-04-23 Mojgan Pourhassan,Feng Shi,Frank Neumann
Evolutionary multiobjective optimization for the classical vertex cover problem has been analysed in Kratsch and Neumann (2013) in the context of parameterized complexity analysis. This article extends the analysis to the weighted vertex cover problem in which integer weights are assigned to the vertices and the goal is to find a vertex cover of minimum weight. Using an alternative mutation operator
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Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming. Evol. Comput. (IF 3.933) Pub Date : 2019-03-22 Masanori Suganuma,Masayuki Kobayashi,Shinichi Shirakawa,Tomoharu Nagao
The convolutional neural network (CNN), one of the deep learning models, has demonstrated outstanding performance in a variety of computer vision tasks. However, as the network architectures become deeper and more complex, designing CNN architectures requires more expert knowledge and trial and error. In this article, we attempt to automatically construct high-performing CNN architectures for a given
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A Tandem Evolutionary Algorithm for Identifying Causal Rules from Complex Data. Evol. Comput. (IF 3.933) Pub Date : 2019-02-28 John P Hanley,Donna M Rizzo,Jeffrey S Buzas,Margaret J Eppstein
We propose a new evolutionary approach for discovering causal rules in complex classification problems from batch data. Key aspects include (a) the use of a hypergeometric probability mass function as a principled statistic for assessing fitness that quantifies the probability that the observed association between a given clause and target class is due to chance, taking into account the size of the
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Mirrored Orthogonal Sampling for Covariance Matrix Adaptation Evolution Strategies. Evol. Comput. (IF 3.933) Pub Date : 2019-02-21 Hao Wang,Michael Emmerich,Thomas Bäck
Generating more evenly distributed samples in high dimensional search spaces is the major purpose of the recently proposed mirrored sampling technique for evolution strategies. The diversity of the mutation samples is enlarged and the convergence rate is therefore improved by the mirrored sampling. Motivated by the mirrored sampling technique, this article introduces a new derandomized sampling technique
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Guiding Neuroevolution with Structural Objectives. Evol. Comput. (IF 3.933) Pub Date : 2019-02-15 Kai Olav Ellefsen,Joost Huizinga,Jim Torresen
The structure and performance of neural networks are intimately connected, and by use of evolutionary algorithms, neural network structures optimally adapted to a given task can be explored. Guiding such neuroevolution with additional objectives related to network structure has been shown to improve performance in some cases, especially when modular neural networks are beneficial. However, apart from
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A Revisit of Infinite Population Models for Evolutionary Algorithms on Continuous Optimization Problems. Evol. Comput. (IF 3.933) Pub Date : 2019-02-05 Bo Song,Victor O K Li
Infinite population models are important tools for studying population dynamics of evolutionary algorithms. They describe how the distributions of populations change between consecutive generations. In general, infinite population models are derived from Markov chains by exploiting symmetries between individuals in the population and analyzing the limit as the population size goes to infinity. In this
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Global Convergence of the (1 + 1) Evolution Strategy to a Critical Point. Evol. Comput. (IF 3.933) Pub Date : 2019-01-31 Tobias Glasmachers
We establish global convergence of the (1 + 1) evolution strategy, that is, convergence to a critical point independent of the initial state. More precisely, we show the existence of a critical limit point, using a suitable extension of the notion of a critical point to measurable functions. At its core, the analysis is based on a novel progress guarantee for elitist, rank-based evolutionary algorithms
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Direct Feature Evaluation in Black-Box Optimization Using Problem Transformations. Evol. Comput. (IF 3.933) Pub Date : 2018-12-29 Sobia Saleem,Marcus Gallagher,Ian Wood
Exploratory Landscape Analysis provides sample-based methods to calculate features of black-box optimization problems in a quantitative and measurable way. Many problem features have been proposed in the literature in an attempt to provide insights into the structure of problem landscapes and to use in selecting an effective algorithm for a given optimization problem. While there has been some success
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Extending the "Open-Closed Principle" to Automated Algorithm Configuration. Evol. Comput. (IF 3.933) Pub Date : 2018-12-18 Jerry Swan,Steven Adriænsen,Adam D Barwell,Kevin Hammond,David R White
Metaheuristics are an effective and diverse class of optimization algorithms: a means of obtaining solutions of acceptable quality for otherwise intractable problems. The selection, construction, and configuration of a metaheuristic for a given problem has historically been a manually intensive process based on experience, experimentation, and reasoning by metaphor. More recently, there has been interest
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Gaussian Process Surrogate Models for the CMA Evolution Strategy. Evol. Comput. (IF 3.933) Pub Date : 2018-12-12 Lukáš Bajer,Zbyněk Pitra,Jakub Repický,Martin Holeňa
This article deals with Gaussian process surrogate models for the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES)-several already existing and two by the authors recently proposed models are presented. The work discusses different variants of surrogate model exploitation and focuses on the benefits of employing the Gaussian process uncertainty prediction, especially during the selection
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Automated Algorithm Selection: Survey and Perspectives. Evol. Comput. (IF 3.933) Pub Date : 2018-11-27 Pascal Kerschke,Holger H Hoos,Frank Neumann,Heike Trautmann
It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity
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Latin Hypercube Designs with Branching and Nested Factors for Initialization of Automatic Algorithm Configuration. Evol. Comput. (IF 3.933) Pub Date : 2018-11-27 Simon Wessing,Manuel López-Ibáñez
The configuration of algorithms is a laborious and difficult process. Thus, it is advisable to automate this task by using appropriate automatic configuration methods. The irace method is among the most widely used in the literature. By default, irace initializes its search process via uniform sampling of algorithm configurations. Although better initialization methods exist in the literature, the
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A Meta-Objective Approach for Many-Objective Evolutionary Optimization. Evol. Comput. (IF 3.933) Pub Date : 2018-11-26 Dunwei Gong,Yiping Liu,Gary G Yen
Pareto-based multi-objective evolutionary algorithms experience grand challenges in solving many-objective optimization problems due to their inability to maintain both convergence and diversity in a high-dimensional objective space. Exiting approaches usually modify the selection criteria to overcome this issue. Different from them, we propose a novel meta-objective (MeO) approach that transforms
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Automatic Configuration of Multi-Objective Local Search Algorithms for Permutation Problems. Evol. Comput. (IF 3.933) Pub Date : 2018-11-09 Aymeric Blot,Marie-Éléonore Kessaci,Laetitia Jourdan,Holger H Hoos
Automatic algorithm configuration (AAC) is becoming a key ingredient in the design of high-performance solvers for challenging optimisation problems. However, most existing work on AAC deals with configuration procedures that optimise a single performance metric of a given, single-objective algorithm. Of course, these configurators can also be used to optimise the performance of multi-objective algorithms
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Neuroevolution of a Modular Memory-Augmented Neural Network for Deep Memory Problems. Evol. Comput. (IF 3.933) Pub Date : 2018-11-08 Shauharda Khadka,Jen Jen Chung,Kagan Tumer
We present Modular Memory Units (MMUs), a new class of memory-augmented neural network. MMU builds on the gated neural architecture of Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTMs), to incorporate an external memory block, similar to a Neural Turing Machine (NTM). MMU interacts with the memory block using independent read and write gates that serve to decouple the memory from the
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Landscape Analysis of a Class of NP-Hard Binary Packing Problems. Evol. Comput. (IF 3.933) Pub Date : 2018-10-27 Khulood Alyahya,Jonathan E Rowe
This article presents an exploratory landscape analysis of three NP-hard combinatorial optimisation problems: the number partitioning problem, the binary knapsack problem, and the quadratic binary knapsack problem. In the article, we examine empirically a number of fitness landscape properties of randomly generated instances of these problems. We believe that the studied properties give insight into
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Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Learning. Evol. Comput. (IF 3.933) Pub Date : 2018-10-27 Pascal Kerschke,Heike Trautmann
In this article, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focusing on algorithm performance results of the COCO platform of several years, we construct a representative
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A Model-based Framework for Black-box Problem Comparison Using Gaussian Processes. Evol. Comput. (IF 3.933) Pub Date : 2018-10-27 Sobia Saleem,Marcus Gallagher,Ian Wood
An important challenge in black-box optimization is to be able to understand the relative performance of different algorithms on problem instances. This challenge has motivated research in exploratory landscape analysis and algorithm selection, leading to a number of frameworks for analysis. However, these procedures often involve significant assumptions, or rely on information not typically available
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Hypervolume Subset Selection with Small Subsets. Evol. Comput. (IF 3.933) Pub Date : 2018-10-26 Benoît Groz,Silviu Maniu
The hypervolume subset selection problem (HSSP) aims at approximating a set of n multidimensional points in Rd with an optimal subset of a given size. The size k of the subset is a parameter of the problem, and an approximation is considered best when it maximizes the hypervolume indicator. This problem has proved popular in recent years as a procedure for multiobjective evolutionary algorithms. Efficient
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Search Dynamics on Multimodal Multiobjective Problems. Evol. Comput. (IF 3.933) Pub Date : 2018-09-28 P Kerschke,H Wang,M Preuss,C Grimme,A H Deutz,H Trautmann,M T M Emmerich
We continue recent work on the definition of multimodality in multiobjective optimization (MO) and the introduction of a test bed for multimodal MO problems. This goes beyond well-known diversity maintenance approaches but instead focuses on the landscape topology induced by the objective functions. More general multimodal MO problems are considered by allowing ellipsoid contours for single-objective
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Emergent Solutions to High-Dimensional Multitask Reinforcement Learning. Evol. Comput. (IF 3.933) Pub Date : 2018-06-23 Stephen Kelly,Malcolm I Heywood
Algorithms that learn through environmental interaction and delayed rewards, or reinforcement learning (RL), increasingly face the challenge of scaling to dynamic, high-dimensional, and partially observable environments. Significant attention is being paid to frameworks from deep learning, which scale to high-dimensional data by decomposing the task through multilayered neural networks. While effective
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Theoretical Analysis of Local Search and Simple Evolutionary Algorithms for the Generalized Travelling Salesperson Problem. Evol. Comput. (IF 3.933) Pub Date : 2018-06-22 Mojgan Pourhassan,Frank Neumann
The generalized travelling salesperson problem is an important NP-hard combinatorial optimization problem for which metaheuristics, such as local search and evolutionary algorithms, have been used very successfully. Two hierarchical approaches with different neighbourhood structures, namely a cluster-based approach and a node-based approach, have been proposed by Hu and Raidl (2008) for solving this
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Data-Efficient Design Exploration through Surrogate-Assisted Illumination. Evol. Comput. (IF 3.933) Pub Date : 2018-06-09 Adam Gaier,Alexander Asteroth,Jean-Baptiste Mouret
Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs. In these domains illumination algorithms, such as MAP-Elites, are promising alternatives to classic optimization algorithms because they produce diverse, high-quality solutions in a single run, instead of only a single near-optimal solution. Unfortunately, these algorithms
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Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming. Evol. Comput. (IF 3.933) Pub Date : 2018-06-04 Michaela Drahosova,Lukas Sekanina,Michal Wiglasz
In genetic programming (GP), computer programs are often coevolved with training data subsets that are known as fitness predictors. In order to maximize performance of GP, it is important to find the most suitable parameters of coevolution, particularly the fitness predictor size. This is a very time-consuming process as the predictor size depends on a given application, and many experiments have to
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A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules. Evol. Comput. (IF 3.933) Pub Date : 2018-06-04 Su Nguyen,Yi Mei,Bing Xue,Mengjie Zhang
Designing effective dispatching rules for production systems is a difficult and time-consuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic
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Counterexample-Driven Genetic Programming: Heuristic Program Synthesis from Formal Specifications. Evol. Comput. (IF 3.933) Pub Date : 2018-05-23 Iwo Błądek,Krzysztof Krawiec,Jerry Swan
Conventional genetic programming (GP) can guarantee only that synthesized programs pass tests given by the provided input-output examples. The alternative to such a test-based approach is synthesizing programs by formal specification, typically realized with exact, nonheuristic algorithms. In this article, we build on our earlier study on Counterexample-Based Genetic Programming (CDGP), an evolutionary
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How to Specify a Reference Point in Hypervolume Calculation for Fair Performance Comparison. Evol. Comput. (IF 3.933) Pub Date : 2018-05-23 Hisao Ishibuchi,Ryo Imada,Yu Setoguchi,Yusuke Nojima
The hypervolume indicator has frequently been used for comparing evolutionary multi-objective optimization (EMO) algorithms. A reference point is needed for hypervolume calculation. However, its specification has not been discussed in detail from a viewpoint of fair performance comparison. A slightly worse point than the nadir point is usually used for hypervolume calculation in the EMO community.
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Anatomy of the Attraction Basins: Breaking with the Intuition. Evol. Comput. (IF 3.933) Pub Date : 2018-05-22 Leticia Hernando,Alexander Mendiburu,Jose A Lozano
Solving combinatorial optimization problems efficiently requires the development of algorithms that consider the specific properties of the problems. In this sense, local search algorithms are designed over a neighborhood structure that partially accounts for these properties. Considering a neighborhood, the space is usually interpreted as a natural landscape, with valleys and mountains. Under this
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On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism. Evol. Comput. (IF 3.933) Pub Date : 2018-05-10 Edgar Covantes Osuna,Dirk Sudholt
Clearing is a niching method inspired by the principle of assigning the available resources among a niche to a single individual. The clearing procedure supplies these resources only to the best individual of each niche: the winner. So far, its analysis has been focused on experimental approaches that have shown that clearing is a powerful diversity-preserving mechanism. Using rigorous runtime analysis
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A Probabilistic and Multi-Objective Analysis of Lexicase Selection and ε-Lexicase Selection. Evol. Comput. (IF 3.933) Pub Date : 2018-05-10 William La Cava,Thomas Helmuth,Lee Spector,Jason H Moore
Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection. Whereas previous work has demonstrated the ability of lexicase selection to solve difficult problems in program synthesis and symbolic regression, the central goal of this article is to develop the theoretical underpinnings that explain its performance
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Hyperplane-Approximation-Based Method for Many-Objective Optimization Problems with Redundant Objectives. Evol. Comput. (IF 3.933) Pub Date : 2018-05-02 Yifan Li,Hai-Lin Liu,E D Goodman
For a many-objective optimization problem with redundant objectives, we propose two novel objective reduction algorithms for linearly and, nonlinearly degenerate Pareto fronts. They are called LHA and NLHA respectively. The main idea of the proposed algorithms is to use a hyperplane with non-negative sparse coefficients to roughly approximate the structure of the PF. This approach is quite different
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Constraint Handling Guided by Landscape Analysis in Combinatorial and Continuous Search Spaces. Evol. Comput. (IF 3.933) Pub Date : 2018-03-13 Katherine M Malan,I Moser
The notion and characterisation of fitness landscapes has helped us understand the performance of heuristic algorithms on complex optimisation problems. Many practical problems, however, are constrained, and when significant areas of the search space are infeasible, researchers have intuitively resorted to a variety of constraint-handling techniques intended to help the algorithm manoeuvre through
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