International audienceMotivated by parallel optimization, we study the Self-Adaptation algorithm for large population sizes. We first show that the current version of this algorithm does not reach the theoretical bounds, then we propose a very simple modification, in the selection part of the evolution process. We show that this simple modification leads to big improvement of the speed-up when the population size is large
We study the (1, λ)-EA with mutation rate c/n for c ≤ 1, where the population size is adaptively con...
Evolutionary algorithms (EAs) are population-based randomized search heuristics that often solve pro...
International audienceWe analyze the performance of the 2-rate (1 + λ) Evolutionary Algorithm (EA) w...
International audienceMotivated by parallel optimization, we study the Self-Adaptation algorithm for...
International audienceMotivated by parallel optimization, we experiment EDA-like adaptation-rules in...
International audienceIt is usually considered that evolutionary algorithms are highly parallel. In ...
peer reviewedThis paper proposes a new selection scheme for Evolutionary Algorithms (EAs) based on a...
International audienceStep-size adaptation for randomised search algorithms like evolution strategie...
International audienceThe goal of this paper is to investigate on the overall performance of CMA-ES,...
International audienceEvolution Strategies (ESs) are population-based methods well suited for parall...
International audienceIn this work we study the effects of population size on selection and performa...
This paper aims to study how the population size affects the computation time of evolutionary algori...
AbstractThe utilization of populations is one of the most important features of evolutionary algorit...
This paper aims to study how the population size affects the computation time of evolutionary algori...
Abstract. Evolutionary Algorithms (EAs) are population-based ran-domized optimizers often solving pr...
We study the (1, λ)-EA with mutation rate c/n for c ≤ 1, where the population size is adaptively con...
Evolutionary algorithms (EAs) are population-based randomized search heuristics that often solve pro...
International audienceWe analyze the performance of the 2-rate (1 + λ) Evolutionary Algorithm (EA) w...
International audienceMotivated by parallel optimization, we study the Self-Adaptation algorithm for...
International audienceMotivated by parallel optimization, we experiment EDA-like adaptation-rules in...
International audienceIt is usually considered that evolutionary algorithms are highly parallel. In ...
peer reviewedThis paper proposes a new selection scheme for Evolutionary Algorithms (EAs) based on a...
International audienceStep-size adaptation for randomised search algorithms like evolution strategie...
International audienceThe goal of this paper is to investigate on the overall performance of CMA-ES,...
International audienceEvolution Strategies (ESs) are population-based methods well suited for parall...
International audienceIn this work we study the effects of population size on selection and performa...
This paper aims to study how the population size affects the computation time of evolutionary algori...
AbstractThe utilization of populations is one of the most important features of evolutionary algorit...
This paper aims to study how the population size affects the computation time of evolutionary algori...
Abstract. Evolutionary Algorithms (EAs) are population-based ran-domized optimizers often solving pr...
We study the (1, λ)-EA with mutation rate c/n for c ≤ 1, where the population size is adaptively con...
Evolutionary algorithms (EAs) are population-based randomized search heuristics that often solve pro...
International audienceWe analyze the performance of the 2-rate (1 + λ) Evolutionary Algorithm (EA) w...