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48 papers · 1 filter
Selection Hyper-heuristics Can Automatically Adjust the Learning Period to Optimally Solve Pseudo-Boolean Problems
Benjamin Doerr, Pietro S. Oliveto, John Alasdair Warwicker
The Random Gradient hyper-heuristic was recently shown to be able to learn the optimal neighbourhood size when optimizing the LeadingOnes benchmark via the Randomised Local Search…
A Fresh Look at Lamarckian Evolution and the Baldwin Effect
Inès Benito, Johannes F. Lutzeyer, Benjamin Doerr
Baldwinian and Lamarckian evolution have existed for a long time in evolutionary algorithms (EAs) without ever dominating the academic literature or practical applications. In this…
Runtime Analysis of the Compact Genetic Algorithm on the LeadingOnes Benchmark
Marcel Chwiałkowski, Benjamin Doerr, Martin S. Krejca
The compact genetic algorithm (cGA) is one of the simplest estimation-of-distribution algorithms (EDAs). Next to the univariate marginal distribution algorithm (UMDA) -- another si…
Speeding Up the NSGA-II With a Simple Tie-Breaking Rule
Benjamin Doerr, Tudor Ivan, Martin S. Krejca
The non-dominated sorting genetic algorithm~II (NSGA-II) is the most popular multi-objective optimization heuristic. Recent mathematical runtime analyses have detected two shortcom…
Runtime Analysis for Multi-Objective Evolutionary Algorithms in Unbounded Integer Spaces
Benjamin Doerr, Martin S. Krejca, Günter Rudolph
Randomized search heuristics have been applied successfully to a plethora of problems. This success is complemented by a large body of theoretical results. Unfortunately, the vast…
Difficulties of the NSGA-II with the Many-Objective LeadingOnes Problem
Benjamin Doerr, Dimitri Korkotashvili, Martin S. Krejca
The NSGA-II is the most prominent multi-objective evolutionary algorithm (cited more than 50,000 times). Very recently, a mathematical runtime analysis has proven that this algorit…