Hyper-heuristics are search techniques for selecting, generating, and sequencing (meta)-
heuristics to solve challenging optimization problems. They differ from traditional (meta) …

This paper contains a modern vision of the parallelization techniques used for evolutionary
algorithms (EAs). The work is motivated by two fundamental facts: 1) the different families of …

A need for solving more and more complex problems drives the Evolutionary Computation
community towards advanced models of Evolutionary Algorithms. One such model is the …

This paper presents an experimental study of distributed multipopulation genetic
programming. Using three well-known benchmark problems and one real-life problem, we …

A variety of previous works exist on maintaining population diversity of genetic algorithms
(GAs). Dual-population GA (DPGA) is a type of multipopulation GA (MPGA) that uses an …

Evolutionary algorithms have been reported to be efficient metaheuristics for the
optimization of several NP-Hard combinatorial optimization problems. In addition to their …

Symbolic regression is an important method of data-driven modeling, which can provide
explicit mathematical expressions for data analysis. However, the existing genetic …

A new parallel implementation of genetic programming (GP) based on the cellular model is
presented and compared with both canonical GP and the island model approach. The …

Solving complex real-world problems using evo-lutionary computation is a CPU time-
consuming task that requires a large amount of computational resources. Peerto-Peer (P2P) …

This paper is aimed at enhancing the success rate of Genetic Programming (GP) algorithms
for symbolic regressions. It is shown that the outcome of GP algorithms over several runs …