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Rethinking Exploration, Exploitation, Selection, and the Escape from Local Optimum:

A review of formal and informal definitions for key concepts in Evolutionary Computation and Swarm Intelligence

Abstract

Many important concepts in Evolutionary Computation and Swarm Intelligence are often discussed with imprecise, inaccurate, and/or conflicting definitions. This tutorial will review the usage of terms such as Exploration, Exploitation, Attraction Basin, Selection, and Escape from Local Optimum that are commonly used in the research literature. Examples of contradictions will be shown and key formal definitions will be analyzed. An in-depth case study will be presented which shows how a popular metaheuristic exists only because of inaccurate definitions and how the deletion of its key component leads to no significant changes in performance.

Learning Objectives
  • Understand the vagueness and imprecision of key terminology used in the field
  • Study specific examples of how poor definitions have led to important misunderstandings or misguided research in the field
  • Analyze several options for precise definitions of key terminology
  • Commit to using key terms with only precise and consistent meanings
Outline
  • Review of Exploration and Exploitation
  • Review of Attraction Basins
  • Review of Selection
  • Review of Escape from Local Optimum
  • Formal definitions for Attraction Basins, Exploration, and Exploitation
  • Case study on Simulated Annealing in Continuous Domains
  • Implications for Future Research in Evolutionary Computation and Swarm Intelligence

Presenters

Marjan Mernik Image

Marjan Mernik is a Professor at the University of Maribor, Faculty of Electrical Engineering and Computer Science. He was a visiting professor at the University of Alabama at Birmingham, Department of Computer and Information Sciences. His research interests include programming languages, domain-specific (modelling) languages, grammar and semantic inference, and evolutionary computation. He is the Editor-in-Chief of the Journal of Computer Languages, as well as an Associate Editor of the Applied Soft Computing Journal and Swarm and Evolutionary Computation Journal. He has been named a Highly Cited Researcher for the years 2017 and 2018.

Stephen Chen Image

Stephen Chen is an Associate Professor in the School of Information Technology at York University, Toronto, Canada. His research focuses on analyzing the mechanisms for selection, exploration, and exploitation in techniques designed for multi-modal optimization problems. He is particularly interested in the development and analysis of non-metaphor-based heuristic search techniques. He has 80 peer-reviewed publications including 30 CEC papers, and he has previously presented 3 tutorials and organized 2 workshops at CEC/WCCI conferences.