Paper Info
Title
Benchmarking the differential evolution with adaptive encoding on noiseless functions
Abstract
The differential evolution (DE) algorithm is equipped with the recently proposed adaptive encoding (AE) which makes the algorithm rotationally invariant. The resulting algorithm, DEAE, should exhibit better performance on non-separable functions. The aim of this article is to assess what benefits the AE has, and what effect it has for other function groups. DEAE is compared against pure DE, an adaptive version of DE (JADE), and an evolutionary strategy with covariance matrix adaptation (CMA-ES). The results suggest that AE indeed improves the performance of DE, particularly on the group of unimodal non-separable functions, but the adaptation of parameters used in JADE is more profitable on average. The use of AE inside JADE is envisioned.
Year
DOI
Venue
2012
10.1145/2330784.2330813
GECCO (Companion)
Keywords
Field
DocType
profitability,evolutionary strategy,differential evolution,covariance matrix adaptation,evolution strategy
Mathematical optimization,Computer science,Differential evolution,Evolution strategy,CMA-ES,Artificial intelligence,Invariant (mathematics),Benchmarking,Machine learning,Encoding (memory)
Conference
Citations 
PageRank 
References 
7
0.51
6
Authors
2
Name
Order
Citations
PageRank
Petr Pošík121015.44
Václav Klemš2171.18