AUTHORS: Julian Scott Yeomans

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ABSTRACT: Complex problems are frequently inundated with incompatible performance requirements and inconsistent performance specifications that can be difficult – if not impossible – to identify at the time of problem formulation. Consequently, it is often advantageous to create a set of dissimilar options that provide distinct approaches to the problem. These disparate alternatives need to be close-to-optimal with respect to the specified objective(s), but remain maximally different from each other in the decision domain. The approach for creating such maximally different solution sets is referred to as modelling-to-generate-alternatives (MGA). This paper provides a new, bicriterion MGA approach that can generate sets of maximally different alternatives using any population-based algorithm.

KEYWORDS: Bicriterion Objectives, Population-based algorithms, Modelling-to-generate-alternatives

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[24] J.S. Yeomans, An Algorithm for Generating Sets of Maximally Different Alternatives Using Population-Based Metaheuristic Procedures, Transactions on Machine Learning and Artificial Intelligence, Vol. 6, No. 5, 2018, pp. 1-9.