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Evolution and optimum seeking
  • H. Schwefel
  • Computer Science
    Sixth-generation computer technology series
  • 9 January 1995
Problems and Methods of Optimization Hill Climbing Strategies Random Strategies Evolution Strategies for Numerical Optimization Comparison of Direct Search Strategies for Parameter Optimization.
Evolution strategies – A comprehensive introduction
This article gives a comprehensive introduction into one of the main branches of evolutionary computation – the evolution strategies (ES) the history of which dates back to the 1960s in Germany.…
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An Overview of Evolutionary Algorithms for Parameter Optimization
Three main streams of evolutionary algorithms (EAs), probabilistic optimization algorithms based on the model of natural evolution, are compared in this article: evolution strategies (ESs),…
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Evolutionary computation: comments on the history and current state
TLDR
The purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA), evolution strategies (ES), and evolutionary programming (EP) are described by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism).
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Numerical optimization of computer models
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A Survey of Evolution Strategies
TLDR
Evolution Strategies are algorithms which imitate the principles of natural evolution as a method to solve parameter optimization problems and adaptation of the strategy parameters for the mutation variances as well as their covariances are described.
Evolution and Optimum Seeking: The Sixth Generation
TLDR
The most comprehensive work of its kind, Evolution and Optimum Seeking offers a state-of-the-art perspective on the field for researchers in computer-aided design, planning, control, systems analysis, computational intelligence, and artificial life.
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
TLDR
The main objective of this preliminary study is the answer to the question whether the predator-prey approach to multi-objective optimization works at all, which is examined under several step-size adaptation rules.
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Parallel Problem Solving from Nature PPSN VI
TLDR
It is found that, with the same neighborhood, rectangular grids have some advantages in multimodal and epistatic problems, while square ones are more efficient for solving deceptive problems and for simple function optimization.
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Evolutionary computation: an overview
TLDR
An overview of the most important representatives of algorithms gleaned from natural evolution, so-called evolutionary algorithms, are presented, with special emphasis on the principle of strategy parameter self-adaptation utilized by the first two algorithms to learn their own strategy parameters such as mutation variances and covariances.
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