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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Analysis of Randomised Search Heuristics for Dynamic Optimisation.

Thomas Jansen1, Christine Zarges2

  • 1Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK t.jansen@aber.ac.uk.

Evolutionary Computation
|August 5, 2015
PubMed
Summary

This study introduces a new framework and example problems for dynamic optimization, enhancing theoretical analysis for evolutionary algorithms and artificial immune systems.

Keywords:
Dynamic optimisation problemsartificial immune systemsevolutionary algorithmsfixed budget computationstheory

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization

Background:

  • Dynamic optimization is a key area for randomized search heuristics like evolutionary algorithms (EAs) and artificial immune systems (AIS).
  • Current theoretical foundations lack a universally accepted analytical framework and standard example problems.
  • This gap hinders rigorous analysis and comparison of these algorithms in dynamic environments.

Purpose of the Study:

  • To address the lack of theoretical frameworks and benchmark problems in dynamic optimization.
  • To propose necessary conditions for effective theoretical analysis of dynamic optimization problems.
  • To introduce a novel family of bi-stable dynamic problems for algorithm testing.

Main Methods:

  • Developed a theoretical framework for analyzing dynamic optimization.
  • Introduced a new class of dynamic optimization problems inspired by a static benchmark, exhibiting bi-stable dynamics.
  • Conducted theoretical and statistical analyses of mutation-based EAs and AIS on these new problems.

Main Results:

  • Established conditions for practical and relevant theoretical analysis in dynamic optimization.
  • Presented a concrete family of bi-stable dynamic problems suitable for testing algorithms.
  • Provided detailed theoretical and statistical results for EAs and AIS, demonstrating the framework's utility.

Conclusions:

  • The proposed framework and problems facilitate more robust theoretical analysis of randomized search heuristics in dynamic optimization.
  • The study provides a foundation for future research in dynamic optimization algorithm development and evaluation.
  • This work bridges the gap between theoretical analysis and practical application of EAs and AIS in dynamic settings.