Territorial Differential Meta-Evolution: An Algorithm for Seeking All the Desirable Optima of a Multivariable Function

Richard Wehr; Scott R Saleska

doi:10.1162/evco_a_00337

Territorial Differential Meta-Evolution: An Algorithm for Seeking All the Desirable Optima of a Multivariable Function

Evol Comput. 2023 Jun 30:1-31. doi: 10.1162/evco_a_00337. Online ahead of print.

Authors

Richard Wehr¹, Scott R Saleska²

Affiliations

¹ Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, U.S.A. Current: Aerodyne Research, Inc., Billerica, MA, 01821, U.S.A.
² Aerodyne Research, Inc., Billerica, MA, 01821, U.S.A. rwehr@aerodyne.com.

PMID: 37390219
DOI: 10.1162/evco_a_00337

Abstract

Territorial Differential Meta-Evolution (TDME) is an efficient, versatile, and reliable algorithm for seeking all the global or desirable local optima of a multivariable function. It employs a progressive niching mechanism to optimize even challenging, highdimensional functions with multiple global optima and misleading local optima. This article introduces TDME and uses standard and novel benchmark problems to quantify its advantages over HillVallEA, which is the best-performing algorithm on the standard benchmark suite that has been used by all major multimodal optimization competitions since 2013. TDME matches HillVallEA on that benchmark suite and categorically outperforms it on a more comprehensive suite that better reflects the potential diversity of optimization problems. TDME achieves that performance without any problem-specific parameter tuning.

Keywords: Differential Evolution; Function optimization; niching.