Many meta-heuristics attempt to “transition” a single algorithm from exploration to exploitation. Conversely, previous research has shown that it can be better for the two distinct tasks of exploration and exploitation to instead be performed by two distinct algorithms/mechanisms. This has led to the development of Exploration-only, Exploitation-only Hybrid search techniques. This paper presents a Multi-Population Exploration only Exploitation-only Hybrid in which exploitation occurs in one population while a global search strategy performs exploration in another population. Unlike a sequential hybrid, this hybridization allows the exploratory technique (in this case Unbiased Exploratory Search) to delay convergence (up to indefinitely) whi...
In this paper a new search strategy for multi-objective optimization (MOO) with constraints is propo...
In this paper, a hybrid particle swarm evolutionary algorithm is proposed for solving constrained mu...
Minimum Population Search is a recently developed metaheuristic for optimization of mono-objective c...
Two factors affect the effectiveness of exploration, the bias introduced by selection and the concur...
Abstract. It is general knowledge that hybrid approaches can improve the performance of search heuri...
A local search method is often introduced in an evolutionary optimization algorithm, to enhance its ...
The file attached to this record is the author's final peer reviewed version. The Publisher's final ...
Minimum Population Search is a new metaheuristic specifically designed for optimizing multi-modal pr...
A local search method is often introduced in an evolutionary optimization algorithm, to enhance its ...
Hybridization of genetic algorithms with local search approaches can en-hance their performance in g...
Population-based heuristics can be effective at optimizing difficult multi-modal problems. However, ...
In this paper we illustrate the use of Population Reinforced Optimization Based Exploration (PROBE) ...
Evolutionary Algorithms are robust and powerful global optimization techniques for solving large sc...
Evolutionary algorithms are robust and powerful global optimization techniques for solving large-sca...
Evolutionary multi-objective optimization algorithms are commonly used to obtain a set of non-domina...
In this paper a new search strategy for multi-objective optimization (MOO) with constraints is propo...
In this paper, a hybrid particle swarm evolutionary algorithm is proposed for solving constrained mu...
Minimum Population Search is a recently developed metaheuristic for optimization of mono-objective c...
Two factors affect the effectiveness of exploration, the bias introduced by selection and the concur...
Abstract. It is general knowledge that hybrid approaches can improve the performance of search heuri...
A local search method is often introduced in an evolutionary optimization algorithm, to enhance its ...
The file attached to this record is the author's final peer reviewed version. The Publisher's final ...
Minimum Population Search is a new metaheuristic specifically designed for optimizing multi-modal pr...
A local search method is often introduced in an evolutionary optimization algorithm, to enhance its ...
Hybridization of genetic algorithms with local search approaches can en-hance their performance in g...
Population-based heuristics can be effective at optimizing difficult multi-modal problems. However, ...
In this paper we illustrate the use of Population Reinforced Optimization Based Exploration (PROBE) ...
Evolutionary Algorithms are robust and powerful global optimization techniques for solving large sc...
Evolutionary algorithms are robust and powerful global optimization techniques for solving large-sca...
Evolutionary multi-objective optimization algorithms are commonly used to obtain a set of non-domina...
In this paper a new search strategy for multi-objective optimization (MOO) with constraints is propo...
In this paper, a hybrid particle swarm evolutionary algorithm is proposed for solving constrained mu...
Minimum Population Search is a recently developed metaheuristic for optimization of mono-objective c...