Add to ORKGThe linkage learning genetic algorithm (LLGA) proposed by Harik (Harik 1997), evolved tight linkage in a bid to solve difficult problems. This paper extends this work to difficult non-stationary problems. The probabilistic expression mechanism of the LLGA is akin to the dominance and polyploidy found in nature. This redundancy of gene expression found in the LLGA was found to benefit the adaptation of the solution to dynamic fitness landscapes. However as the LLGA converges to tight linkage the available diversity decreases considerably. In this study it was found that by allowing disruption of tightly linked structures (with lo
For more than two decades, genetic algorithms (GAs) have been studied by researchers from different ...
Linkage learning techniques are employed to discover dependencies between problem variables. This kn...
The exploitation of linkage learning is enhancing the performance of evolutionary algorithms. This m...
One variance of Genetic Algorithms is a Linkage Learning Genetic Algorithm (LLGA) enhances the effic...
The complicated nature of modern scientific endeavors often times requires the employment of black-b...
Over the last 10 years, many efforts have been made to design a competent genetic algorithm. This pa...
There are two primary objectives of this dissertation. The first goal is to identify certain limits ...
The Gene expression messy genetic algorithm (GEMGA) is a new generation of messy genetic algorithms...
Discovering and exploiting the linkage between genes during evolutionary search allows the Linkage T...
The recently introduced Linkage Tree Genetic Algorithm (LTGA) has shown to exhibit excellent scalabi...
Problem-specific knowledge is often implemented in search algorithms using heuristics to determine w...
The linkage tree genetic algorithm (LTGA) learns, each generation, a linkage model by building a hie...
htmlabstractLinkage-learning Evolutionary Algorithms (EAs) use linkage learning to construct a link...
Intelligent guessing plays a critical role in the success and scalability of a nonenumerative optimi...
Genepool Optimal Mixing Evolutionary Algorithms (GOMEAs) were recently proposed as a new way of desi...
For more than two decades, genetic algorithms (GAs) have been studied by researchers from different ...
Linkage learning techniques are employed to discover dependencies between problem variables. This kn...
The exploitation of linkage learning is enhancing the performance of evolutionary algorithms. This m...
One variance of Genetic Algorithms is a Linkage Learning Genetic Algorithm (LLGA) enhances the effic...
The complicated nature of modern scientific endeavors often times requires the employment of black-b...
Over the last 10 years, many efforts have been made to design a competent genetic algorithm. This pa...
There are two primary objectives of this dissertation. The first goal is to identify certain limits ...
The Gene expression messy genetic algorithm (GEMGA) is a new generation of messy genetic algorithms...
Discovering and exploiting the linkage between genes during evolutionary search allows the Linkage T...
The recently introduced Linkage Tree Genetic Algorithm (LTGA) has shown to exhibit excellent scalabi...
Problem-specific knowledge is often implemented in search algorithms using heuristics to determine w...
The linkage tree genetic algorithm (LTGA) learns, each generation, a linkage model by building a hie...
htmlabstractLinkage-learning Evolutionary Algorithms (EAs) use linkage learning to construct a link...
Intelligent guessing plays a critical role in the success and scalability of a nonenumerative optimi...
Genepool Optimal Mixing Evolutionary Algorithms (GOMEAs) were recently proposed as a new way of desi...
For more than two decades, genetic algorithms (GAs) have been studied by researchers from different ...
Linkage learning techniques are employed to discover dependencies between problem variables. This kn...
The exploitation of linkage learning is enhancing the performance of evolutionary algorithms. This m...