Abstract—This paper presents an online demo of evolutionary programming using a mixed mutation strategy for solving func-tion optimization problems. The strategy combines three different mutation operators: Gaussian, Cauchy and Lévy mutations. The algorithm has been implemented by a client-server web application, which is convenient for users to access through Internet. The web application architecture is divided into two parts: a client to deal with users ’ input and a server to execute the computation. Experiments have shown that EP using mixed mutation strategies can produce high quality solutions on most of 14 benchmark functions. I
Key to defining effective and efficient optimization algorithms is exploiting problem structure and ...
In this thesis we investigate how intelligent techniques, such as Evolutionary Algorithms, can be ap...
In this study we use Genetic Programming (GP) as an offline hyper-heuristic to evolve a mutation ope...
Different mutation operators such as Gaussian, Cauchy and Lévy mutations have been proposed in evolu...
The performance of Evolutionary Programming (EP) is affected by many factors (e.g. mutation operator...
Evolutionary programming can solve black-box function optimisation problems by evolving a population...
Abstract. Different mutation operators have been proposed in evolutionary programming. However, each...
Abstract—Evolutionary Programming (EP) has been modi-fied in various ways. In particular, modificati...
Evolutionary processes have attracted considerable interest in recent years for solving a variety of...
In this study we use Genetic Programming (GP) as an offline hyper-heuristic to evolve a mutation ope...
This book is intended as a reference both for experienced users of evolutionary algorithms and for r...
Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are ...
(EP) is affected by many factors (e.g. mutation operators and selection strategies). Although the co...
Abstract. The mutation operator is the only source of variation in Evo-lutionary Programming. In the...
In this paper, we present an overview of the most important representatives of algorithms gleaned fr...
Key to defining effective and efficient optimization algorithms is exploiting problem structure and ...
In this thesis we investigate how intelligent techniques, such as Evolutionary Algorithms, can be ap...
In this study we use Genetic Programming (GP) as an offline hyper-heuristic to evolve a mutation ope...
Different mutation operators such as Gaussian, Cauchy and Lévy mutations have been proposed in evolu...
The performance of Evolutionary Programming (EP) is affected by many factors (e.g. mutation operator...
Evolutionary programming can solve black-box function optimisation problems by evolving a population...
Abstract. Different mutation operators have been proposed in evolutionary programming. However, each...
Abstract—Evolutionary Programming (EP) has been modi-fied in various ways. In particular, modificati...
Evolutionary processes have attracted considerable interest in recent years for solving a variety of...
In this study we use Genetic Programming (GP) as an offline hyper-heuristic to evolve a mutation ope...
This book is intended as a reference both for experienced users of evolutionary algorithms and for r...
Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are ...
(EP) is affected by many factors (e.g. mutation operators and selection strategies). Although the co...
Abstract. The mutation operator is the only source of variation in Evo-lutionary Programming. In the...
In this paper, we present an overview of the most important representatives of algorithms gleaned fr...
Key to defining effective and efficient optimization algorithms is exploiting problem structure and ...
In this thesis we investigate how intelligent techniques, such as Evolutionary Algorithms, can be ap...
In this study we use Genetic Programming (GP) as an offline hyper-heuristic to evolve a mutation ope...