In the crowded environment of bio-inspired population-based metaheuristics, the Salp
Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of …

Benchmarking and performance analysis play an important role in understanding the
behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic …

Automated benchmarking environments aim to support researchers in understanding how
different algorithms perform on different types of optimization problems. Such comparisons …

Recent theoretical research has shown that self-adjusting and self-adaptive mechanisms
can provably outperform static settings in evolutionary algorithms for binary search spaces …

Computational Intelligence methods, which include Evolutionary Computation and Swarm
Intelligence, can efficiently and effectively identify optimal solutions to complex optimization …

The number of proposed iterative optimization heuristics is growing steadily, and with this
growth, there have been many points of discussion within the wider community. One …

A key property underlying the success of evolutionary algorithms (EAs) is their global search
behavior, which allows the algorithms to" jump" from a current state to other parts of the …

The optimization of combinatorial black-box functions is pervasive in computer science and
engineering. However, the combinatorial explosion of the search space and lack of natural …

Runtime analysis aims at contributing to our understanding of evolutionary algorithms
through mathematical analyses of their runtimes. In the context of discrete optimization …

We investigate a family of (μ+ λ) Genetic Algorithms (GAs) which creates offspring either
from mutation or by recombining two randomly chosen parents. By scaling the crossover …