An intuitive distance-based explanation of opposition-based sampling

  • Authors:

  • Shahryar Rahnamayan;G. Gary Wang;Mario Ventresca

  • Affiliations:

  • Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, Ontario, Canada L1H 7K4;School of Engineering Science, Simon Fraser University, 250-13450 102 Avenue Surrey, BC, Canada V3T 0A3;Center for Pathogen Evolution, Department of Zoology, University of Cambridge, Downing St., Cambridge CB2 3EJ, UK and Department of Mechanical and Industrial Engineering, 5 King's College Road, To ...

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2012

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Abstract

The impact of the opposition concept can be observed in many areas around us. This concept has sometimes been called by different names, such as, opposite particles in physics, complement of an event in probability, absolute or relative complement in set theory, and theses and antitheses in dialectic. Recently, opposition-based learning (OBL) was proposed and has been utilized in different soft computing areas. The main idea behind OBL is the simultaneous consideration of a candidate and its corresponding opposite candidate in order to achieve a better approximation for the current solution. OBL has been employed to introduce opposition-based optimization, opposition-based reinforcement learning, and opposition-based neural networks, as some examples among others. This work proposes an Euclidean distance-to-optimal solution proof that shows intuitively why considering the opposite of a candidate solution is more beneficial than another random solution. The proposed intuitive view is generalized to N-dimensional search spaces for black-box problems.