Work

Comparing multimodal optimization and illumination

Year: 2017

Type: conference-paper

Abstract: Illumination algorithms are a recent addition to the evolutionary computation toolbox that allows the generation of many diverse and high-performing solutions in a single run. Nevertheless, traditional multimodal optimization algorithms also search for diverse and high-performing solutions: could some multimodal optimization algorithms be better at illumination than illumination algorithms? In this study, we compare two illumination algorithms (Novelty Search with Local Competition (NSLC), MAP-Elites) with two multimodal optimization ones (Clearing, Restricted Tournament Selection) in a maze navigation task. The results show that Clearing can have comparable performance to MAP-Elites and NSLC.

Source: Genetic and Evolutionary Computation Conference Companion (GECCO Companion)

DOI: 10.1145/3067695.3075610

Authors: Vassilis Vassiliades, Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret

Institution: Université de Lorraine

Language: English

Topic: Evolutionary Algorithms and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2645953700

FWCI: 0.984

Cites: 12

Cited by: 11

Related to: 10