An Algorithm for Generating Sets of Maximally Different Alternatives Using Population-Based Metaheuristic Procedures

Authors

  • Julian Scott Yeomans York University, Schulich School of Business

DOI:

https://doi.org/10.14738/tmlai.65.5184

Keywords:

Modelling-to-generate-alternatives, Metaheuristics, Population-based algorithms

Abstract

“Real world” problems typically possess complex performance conditions peppered with inconsistent performance requirements. This situation occurs because multifaceted problems are often riddled with incompatible performance objectives and contradictory design requirements which can be difficult – if not impossible – to specify when the requisite decision models are formulated. Thus, it is often desirable to generate a set of disparate alternatives that provide diverse approaches to the problem. These dissimilar options should be close-to-optimal with respect to any specified objective(s), but remain maximally different from all other solutions in the decision space. The approach for creating maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). This paper outlines an MGA algorithmic approach that can simultaneously generate a set of maximally different alternatives using any population-based metaheuristic.

Author Biography

Julian Scott Yeomans, York University, Schulich School of Business

Operations Management and Information Systems Area, Professor

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Published

2018-11-03

How to Cite

Yeomans, J. S. (2018). An Algorithm for Generating Sets of Maximally Different Alternatives Using Population-Based Metaheuristic Procedures. Transactions on Machine Learning and Artificial Intelligence, 6(5), 01. https://doi.org/10.14738/tmlai.65.5184