The CEPA, an Evolutionary Algorithm that preserves diversity by finding clusters in the population, has had its convergence performance improved by a technique tentatively called ‘evolutionary operator selection’. Performance is compared to results found in the literature, though at the moment it is not entirely clear how the evolutionary operator selection mechanisms work. The resulting algorithm has been applied to a number of problems - including a hybrid vehicle configuration and coke production in Shanxi Province, China
Research on multi-objective evolutionary algorithms (MOEAs) has produced over the past decades a lar...
We describe a hybrid and adaptive coevolutionary optimization method that can efficiently solve a wi...
International audienceMaster-slave parallelization of Evolutionary Algorithms (EAs) is straightforwa...
Abstract. The CPEA, an Evolutionary Algorithm that preserves diversity by find-ing clusters in the p...
This paper introduces a multi-objective EA, termed the Clustering Pareto Evolutionary Algorithm (CPE...
Parent selection in evolutionary algorithms for multi-objective optimization is usually performed by...
Multi-objective optimization has become mainstream because several real-world problems are naturally...
Abstract: Multi-objective EAs (MOEAs) are well established population-based techniques for solving v...
Avec la sévérisation des réglementations environnementales sur les émissions polluantes (normes Euro...
Parent selection in evolutionary algorithms for multi-objective optimisation is usually performed by...
Real-world problems commonly require the simultaneous consideration of multiple, often conflicting, ...
Parent selection in evolutionary algorithms for multi-objective optimization is usually performed by...
Selection is a major driving force behind evolution and is a key feature of multiobjective evolution...
A convergence acceleration operator (CAO) is described which enhances the search capability and the ...
Purpose – One of the main components of multi-objective, and therefore, many-objective evolutionary ...
Research on multi-objective evolutionary algorithms (MOEAs) has produced over the past decades a lar...
We describe a hybrid and adaptive coevolutionary optimization method that can efficiently solve a wi...
International audienceMaster-slave parallelization of Evolutionary Algorithms (EAs) is straightforwa...
Abstract. The CPEA, an Evolutionary Algorithm that preserves diversity by find-ing clusters in the p...
This paper introduces a multi-objective EA, termed the Clustering Pareto Evolutionary Algorithm (CPE...
Parent selection in evolutionary algorithms for multi-objective optimization is usually performed by...
Multi-objective optimization has become mainstream because several real-world problems are naturally...
Abstract: Multi-objective EAs (MOEAs) are well established population-based techniques for solving v...
Avec la sévérisation des réglementations environnementales sur les émissions polluantes (normes Euro...
Parent selection in evolutionary algorithms for multi-objective optimisation is usually performed by...
Real-world problems commonly require the simultaneous consideration of multiple, often conflicting, ...
Parent selection in evolutionary algorithms for multi-objective optimization is usually performed by...
Selection is a major driving force behind evolution and is a key feature of multiobjective evolution...
A convergence acceleration operator (CAO) is described which enhances the search capability and the ...
Purpose – One of the main components of multi-objective, and therefore, many-objective evolutionary ...
Research on multi-objective evolutionary algorithms (MOEAs) has produced over the past decades a lar...
We describe a hybrid and adaptive coevolutionary optimization method that can efficiently solve a wi...
International audienceMaster-slave parallelization of Evolutionary Algorithms (EAs) is straightforwa...