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Interactive chaotic evolution

Interactive chaotic evolution

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In this chapter, we propose a new interactive evolutionary computation (IEC) algorithm, we call it interactive chaotic evolution (ICE), which fuses the optimization capability of chaotic evolution and the subjective evaluation of human. We make a brief review on the research of IEC optimization and present the philosophy and the implementation of chaotic evolution and some chaotic systems that can be involved in ICE optimization framework. For a comparison study of ICE, we introduce the interactive differential evolution algorithm (IDE) as a comparative algorithm. We investigate the optimization performance of ICE using some benchmark functions as pseudo-IEC evaluators, and several statistical tests are applied. We analyse and discuss the subjects on paired comparison mechanism of ICE, the distribution characteristic of chaotic system and optimization capability of ICE, the characteristics of combining a chaotic system and a uniform random system, and fusion of ICE and IDE. In this work, we do not only pursue to analyse and discuss the algorithm optimization mechanism of ICE but also induce the philosophy and methodology behind it. We hope that many people realize that the capability of ICE is not only optimization from these efforts, and ICE can make benefit to both chaos theory and evolutionary optimization, perspectively.

Chapter Contents:

  • Abstract
  • 15.1 Introduction
  • 15.1.1 Interactive evolutionary computation
  • 15.1.2 Motivation of interactive chaotic evolution
  • 15.1.3 Contents and structures
  • 15.2 Chaotic evolution towards methodology and philosophy of chaotic optimization
  • 15.2.1 Philosophy of chaotic evolution
  • 15.2.2 Implementation of chaotic evolution
  • 15.2.2.1 Chaotic vector implementing search function
  • 15.2.2.2 Direction factor guiding search directions
  • 15.2.2.3 Chaotic parameter from a chaotic system
  • 15.2.3 Perspective of chaotic evolution
  • 15.3 Chaotic systems implementing basic search in chaotic evolution algorithm framework
  • 15.3.1 Logistic map
  • 15.3.2 Tent map
  • 15.3.3 Gaussian map
  • 15.3.4 Hénon map
  • 15.4 Interactive differential evolution: a paired comparison-based interactive evolutionary computation algorithm
  • 15.4.1 Paired comparison-based interactive evolutionary computation
  • 15.4.2 Paired comparison-based interactive differential evolution
  • 15.4.3 Difference between differential evolution algorithm and chaotic evolution algorithm
  • 15.5 Interactive chaotic evolution: fusion of chaotic evolution optimization capability and human subjective evaluation
  • 15.5.1 Initialization of chaotic evolution algorithm
  • 15.5.2 Mutation and crossover operations
  • 15.5.3 Selection operation
  • 15.5.4 Interactive chaotic evolution algorithm framework
  • 15.6 Evaluations and discussions of interactive chaotic evolution algorithm
  • 15.6.1 Experimental settings and results
  • 15.6.2 Interactive chaotic evolution and its paired comparison mechanism
  • 15.6.3 Distribution characteristic of chaotic system and optimization capability of interactive chaotic evolution
  • 15.6.4 Abelian group of combining a chaotic system and a uniform random system
  • 15.6.5 Fusion of chaotic evolution and differential evolution
  • 15.7 Conclusion
  • References

Inspec keywords: evolutionary computation; optimisation; chaos

Other keywords: chaos theory; paired comparison mechanism; chaotic system; evolutionary optimization; ICE optimization framework; IEC optimization; algorithm optimization mechanism; statistical test; optimization performance; interactive differential evolution algorithm; interactive evolutionary computation algorithm; IDE; pseudoIEC evaluators; interactive chaotic evolution; uniform random system

Subjects: Optimisation; Optimisation techniques; Optimisation techniques

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