Work

The (1 + (λ, λ)) global SEMO algorithm

Year: 2022

Type: conference-paper

Abstract: The (1 + (λ, λ)) genetic algorithm is a recently proposed single-objective evolutionary algorithm with several interesting properties. We show that its main working principle, mutation with a high rate and crossover as repair mechanism, can be transported also to multi-objective evolutionary computation. We define the (1 + (λ, λ)) global SEMO algorithm, a variant of the classic global SEMO algorithm, and prove that it optimizes the OneMinMax benchmark asymptotically faster than the global SEMO. Following the single-objective example, we design a one-fifth rule inspired dynamic parameter setting (to the best of our knowledge for the first time in discrete multi-objective optimization) and prove that it further improves the runtime to O(n2), whereas the best runtime guarantee for the global SEMO is only O(n2 log n).

Source: Genetic and Evolutionary Computation Conference (GECCO)

DOI: 10.1145/3512290.3528868

Authors: Benjamin Doerr, Omar El Hadri, Adrien Pinard

Institution: École Polytechnique

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: LabEx Mathématique Hadamard