Multimodal optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions
in a single simulation run has practical relevance to problem solving across many fields …

This paper reviews the existing literature on the combination of metaheuristics with machine
learning methods and then introduces the concept of learnheuristics, a novel type of hybrid …

We introduce COCO, an open-source platform for Comparing Continuous Optimizers in a
black-box setting. COCO aims at automatizing the tedious and repetitive task of …

This survey compiles ideas and recommendations from more than a dozen researchers with
different backgrounds and from different institutes around the world. Promoting best practice …

In recent years, there has been a growing interest for the experimental analysis in the field of
evolutionary algorithms. It is noticeable due to the existence of numerous papers which …

This paper proposes an orthogonal learning particle swarm optimization (OLPSO) by
designing an orthogonal learning (OL) strategy through the orthogonal experimental design …

In this paper we introduce a restart-CMA-evolution strategy, where the population size is
increased for each restart (IPOP). By increasing the population size the search characteristic …

This paper presents results of the BBOB-2009 benchmarking of 31 search algorithms on 24
noiseless functions in a black-box optimization scenario in continuous domain. The runtime …

In the last years, there has been a growing interest for the experimental analysis in the field
of evolutionary algorithms. It is noticeable due to the existence of numerous papers which …

Quantifying and comparing performance of optimization algorithms is one important aspect
of research in search and optimization. However, this task turns out to be tedious and difficult …