Proceedings ArticleDOI
Evolutionary computation: a unified approach
Kenneth de Jong
- pp 2245-2258
TLDR
An overview of a general EC framework that can help compare and contrast approaches, encourages crossbreeding, and facilitates intelligent design choices is given.Abstract:
The field of Evolutionary Computation has experienced tremendous growth over the past 20 years, resulting in a wide variety of evolutionary algorithms and applications. The result poses an interesting dilemma for many practitioners in the sense that, with such a wide variety of algorithms and approaches, it is often hard to se the relationships between them, assess strengths and weaknesses, and make good choices for new application areas. This tutorial is intended to give an overview of a general EC framework that can help compare and contrast approaches, encourages crossbreeding, and facilitates intelligent design choices. The use of this framework is then illustrated by showing how traditional EAs can be compared and contrasted with it, and how new EAs can be effectively designed using it. Finally, the framework is used to identify some important open issues that need further research.read more
Citations
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Journal ArticleDOI
Differential Evolution: A Survey of the State-of-the-Art
TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.
Journal ArticleDOI
A Comprehensive Survey of Multiagent Reinforcement Learning
TL;DR: The benefits and challenges of MARL are described along with some of the problem domains where the MARL techniques have been applied, and an outlook for the field is provided.
Journal ArticleDOI
Cooperative Multi-Agent Learning: The State of the Art
Liviu Panait,Sean Luke +1 more
TL;DR: This survey attempts to draw from multi-agent learning work in a spectrum of areas, including RL, evolutionary computation, game theory, complex systems, agent modeling, and robotics, and finds that this broad view leads to a division of the work into two categories.
Journal ArticleDOI
Abandoning objectives: Evolution through the search for novelty alone
Joel Lehman,Kenneth O. Stanley +1 more
TL;DR: In the maze navigation and biped walking tasks in this paper, novelty search significantly outperforms objective-based search, suggesting the strange conclusion that some problems are best solved by methods that ignore the objective.
Book ChapterDOI
Computational Intelligence: An Introduction
TL;DR: The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.