Comparing parameter tuning methods for evolutionary algorithms
S.K. Smit,Agoston E. Eiben +1 more
- pp 399-406
Reads0
Chats0
TLDR
The most important issues related to tuning EA parameters are discussed, a number of existing tuning methods are described, and a modest experimental comparison among them are presented, hopefully inspiring fellow researchers for further work.Abstract:
Tuning the parameters of an evolutionary algorithm (EA) to a given problem at hand is essential for good algorithm performance. Optimizing parameter values is, however, a non-trivial problem, beyond the limits of human problem solving.In this light it is odd that no parameter tuning algorithms are used widely in evolutionary computing. This paper is meant to be stepping stone towards a better practice by discussing the most important issues related to tuning EA parameters, describing a number of existing tuning methods, and presenting a modest experimental comparison among them. The paper is concluded by suggestions for future research - hopefully inspiring fellow researchers for further work.read more
Citations
More filters
Journal ArticleDOI
Exploration and exploitation in evolutionary algorithms: A survey
TL;DR: A fresh treatment is introduced that classifies and discusses existing work within three rational aspects: what and how EA components contribute to exploration and exploitation; when and how Exploration and exploitation are controlled; and how balance between exploration and exploited is achieved.
A Exploration and Exploitation in Evolutionary Algorithms: A Survey
TL;DR: In this paper, a good ratio between exploration and exploitation of a search space is defined as the ratio between the probability that a search algorithm is successful and the probability of being successful.
Journal ArticleDOI
Parameter tuning for configuring and analyzing evolutionary algorithms
Agoston E. Eiben,S.K. Smit +1 more
TL;DR: A conceptual framework for parameter tuning is presented, a survey of tuning methods is provided, and related methodological issues are discussed to elaborate on how tuning can improve methodology by facilitating well-funded experimental comparisons and algorithm analysis.
Journal ArticleDOI
A Hybrid Genetic Algorithm for Multidepot and Periodic Vehicle Routing Problems
TL;DR: The metaheuristic combines the exploration breadth of population-based evolutionary search, the aggressive-improvement capabilities of neighborhood-based metaheuristics, and advanced population-diversity management schemes and proves extremely competitive for the capacitated VRP.
Journal ArticleDOI
Bio-inspired computation: Where we stand and what's next
Javier Del Ser,Javier Del Ser,Eneko Osaba,Daniel Molina,Xin-She Yang,Sancho Salcedo-Sanz,David Camacho,Swagatam Das,Ponnuthurai Nagaratnam Suganthan,Carlos A. Coello Coello,Francisco Herrera +10 more
TL;DR: The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.
References
More filters
Introduction to Evolutionary Computing
Agoston E. Eiben,James C. Smith +1 more
TL;DR: In the second edition, the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations as discussed by the authors.
Book
Introduction to evolutionary computing
Agoston E. Eiben,James C. Smith +1 more
TL;DR: The authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations, and added a chapter on evolutionary robotics with an outlook on possible exciting developments in this field.
Journal ArticleDOI
Optimization of Control Parameters for Genetic Algorithms
TL;DR: GA's are shown to be effective for both levels of the systems optimization problem and are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems.
Book
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Pedro Larraanaga,Jose A. Lozano +1 more
TL;DR: This book presents an introduction to Evolutionary Algorithms, a meta-language for programming with real-time implications, and some examples of how different types of algorithms can be tuned for different levels of integration.
Book ChapterDOI
The CMA Evolution Strategy: A Comparing Review
TL;DR: In this review, the argument starts out with large population sizes, reflecting recent extensions of the CMA algorithm, and similarities and differences to continuous Estimation of Distribution Algorithms are analyzed.