This paper proposes a new algorithm which promotes well distributed non-dominated fronts in the parameters space when a single-objective function is optimized. This algorithm is b...
Abstract. In this paper, we apply a competitive coevolutionary approach using loosely coupled genetic algorithms to a distributed optimization of the Rosenbrock's function. Th...
Franciszek Seredynski, Pascal Bouvry, Farhad Arbab
This paper extends previous work showing how fluctuating crosstalk in a deterministic fitness function introduces noise into genetic algorithms. In that work, we modeled fluctuati...
This paper takes an economic approach to derive an evolutionary learning model based entirely on the endogenous employment of genetic operators in the service of self-interested a...
Program-specific or function-specific optimization phase sequences are universally accepted to achieve better overall performance than any fixed optimization phase ordering. A ...