Genetic and Evolutionary Computation Conference (GECCO 2002), New York, USA, 9-13 July 2002THis paper presents a new approach to the field of genetic algorithms, basedon the indroduction of dependency between genes, as inspired by Grammatical Evolution. A system based on that approach, LINKGUAGE, is presented, and results reported show how the dependency between genes creates a tight linkage, guiding the system to success on hard deceptive linkage problems
The Gene expression messy genetic algorithm (GEMGA) is a new generation of messy genetic algorithms...
Linkage learning techniques are employed to discover dependencies between problem variables. This kn...
Discovering and exploiting the linkage between genes during evolutionary search allows the Linkage T...
Genetic Programming: 5th European Conference (EuroGP), Kinsale, Co. Cork, Ireland, 3-5 April 2002Thi...
The complicated nature of modern scientific endeavors often times requires the employment of black-b...
The 2003 Genetic and Evolutionary Computation Conference -(GECCO 2003): The 2nd Grammatical Evolutio...
One variance of Genetic Algorithms is a Linkage Learning Genetic Algorithm (LLGA) enhances the effic...
Learning genetic representation has been shown to be a useful tool in evolutionary computation. It c...
This thesis proposes a new representation for genetic algorithms, based on the idea of a genotype to...
The practical and theoretical success of any Evolutionary Computation (EC) application depends on th...
This paper presents a Machine Learning approach to control genetic algorithms. From examples gathere...
Problem-specific knowledge is often implemented in search algorithms using heuristics to determine w...
This paper explores an area within Evolutionary Computation called Grammatical Evolution [8]. This a...
The linkage learning genetic algorithm (LLGA) proposed by Harik (Harik 1997), evolved tight linkage ...
Problem-specific knowledge is often implemented in search algorithms using heuristics to determine w...
The Gene expression messy genetic algorithm (GEMGA) is a new generation of messy genetic algorithms...
Linkage learning techniques are employed to discover dependencies between problem variables. This kn...
Discovering and exploiting the linkage between genes during evolutionary search allows the Linkage T...
Genetic Programming: 5th European Conference (EuroGP), Kinsale, Co. Cork, Ireland, 3-5 April 2002Thi...
The complicated nature of modern scientific endeavors often times requires the employment of black-b...
The 2003 Genetic and Evolutionary Computation Conference -(GECCO 2003): The 2nd Grammatical Evolutio...
One variance of Genetic Algorithms is a Linkage Learning Genetic Algorithm (LLGA) enhances the effic...
Learning genetic representation has been shown to be a useful tool in evolutionary computation. It c...
This thesis proposes a new representation for genetic algorithms, based on the idea of a genotype to...
The practical and theoretical success of any Evolutionary Computation (EC) application depends on th...
This paper presents a Machine Learning approach to control genetic algorithms. From examples gathere...
Problem-specific knowledge is often implemented in search algorithms using heuristics to determine w...
This paper explores an area within Evolutionary Computation called Grammatical Evolution [8]. This a...
The linkage learning genetic algorithm (LLGA) proposed by Harik (Harik 1997), evolved tight linkage ...
Problem-specific knowledge is often implemented in search algorithms using heuristics to determine w...
The Gene expression messy genetic algorithm (GEMGA) is a new generation of messy genetic algorithms...
Linkage learning techniques are employed to discover dependencies between problem variables. This kn...
Discovering and exploiting the linkage between genes during evolutionary search allows the Linkage T...