7 accepted papers
In recent years, theoretical understanding has rapidly advanced regarding how popular multi-objective evolutionary algorithms (MOEAs) can optimize many-objective problems. However, the benefits of using crossover in many-objective optimization are theoretically not understood, except for specificall
Evolutionary algorithms are widely used for multi-objective optimization, with NSGA-III being particularly effective for problems with more than three objectives, unlike NSGA-II. Despite its empirical success, its theoretical understanding remains limited, especially regarding runtime analysis. A ce
The NSGA-III is a prominent algorithm in evolutionary many-objective optimization. It is well-suited for optimizing functions with more than three objectives, setting it apart from the classic NSGA-II. However, theoretical insights about NSGA-III of when and why it performs well are still in its ear
This paper addresses theory in evolutionary multiobjective optimisation (EMO) and focuses on the role of crossover operators in many-objective optimisation. The advantages of using crossover are hardly understood and rigorous runtime analyses with crossover are lagging far behind its use in practice…
Quality diversity (QD) algorithms, an extension of evolutionary algorithms, excel at generating diverse sets of high-quality solutions for complex problems in robotics, games, and combinatorial optimisation. Despite their success, the underlying mechanisms remain poorly understood due to a lack of a
The global simple evolutionary multi-objective optimizer (GSEMO) is a simple, yet often effective multi-objective evolutionary algorithm (MOEA). By only maintaining non-dominated solutions, it has a variable population size that automatically adjusts to the needs of the optimization process. The dow
Evolutionary algorithms are popular algorithms for multiobjective optimisation (also called Pareto optimisation) as they use a population to store trade-offs between different objectives. Despite their popularity, the theoretical foundation of multiobjective evolutionary optimisation (EMO) is still…