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7 papers · 2 filters
Runtime Analysis of the SMS-EMOA for Many-Objective Optimization
Weijie Zheng, Benjamin Doerr
This paper conducts the first rigorous runtime analysis of the SMS-EMOA for many-objective optimization. To this aim, we first propose a many-objective counterpart of the bi-object…
Bivariate Estimation-of-Distribution Algorithms Can Find an Exponential Number of Optima
Benjamin Doerr, Martin S. Krejca
Finding a large set of optima in a multimodal optimization landscape is a challenging task. Classical population-based evolutionary algorithms typically converge only to a single s…
Runtime Analyses of Multi-Objective Evolutionary Algorithms in the Presence of Noise
Matthieu Dinot, Benjamin Doerr, Ulysse Hennebelle +1
In single-objective optimization, it is well known that evolutionary algorithms also without further adjustments can tolerate a certain amount of noise in the evaluation of the obj…
How the Move Acceptance Hyper-Heuristic Copes With Local Optima: Drastic Differences Between Jumps and Cliffs
Benjamin Doerr, Arthur Dremaux, Johannes Lutzeyer +1
In recent work, Lissovoi, Oliveto, and Warwicker (Artificial Intelligence (2023)) proved that the Move Acceptance Hyper-Heuristic (MAHH) leaves the local optimum of the multimodal…
(1+1) Genetic Programming With Functionally Complete Instruction Sets Can Evolve Boolean Conjunctions and Disjunctions with Arbitrarily Small Error
Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto
Recently it has been proven that simple GP systems can efficiently evolve a conjunction of variables if they are equipped with the minimal required components. In this paper, w…
Estimation-of-Distribution Algorithms for Multi-Valued Decision Variables
Firas Ben Jedidia, Benjamin Doerr, Martin S. Krejca
The majority of research on estimation-of-distribution algorithms (EDAs) concentrates on pseudo-Boolean optimization and permutation problems, leaving the domain of EDAs for proble…