Crossover operator plays a crucial role in the efficiency of genetic algorithm (GA). Several crossover operators have been proposed for solving the travelling salesman problem (TSP) in the literature. These operators have paid less attention to the characteristics of the traveling salesman problem, and majority of these operators can only generate feasible solutions. In this paper, a crossover ...

Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing numbe...

We investigate the utility of weighting the crossover points in genetic programming. The depth-fair crossover (DFC) operator is introduced as an alternative to the standard 90=10 weight heuristic. The DFC weight heuristic performs better that the standard 90=10 weight heuristic in the clique domain. Preliminary results also indicate it will perform better in other applications.

In evolutionary algorithms, crossover operators are used to recombine multiple candidate solutions to yield a new solution that hopefully inherits good genetic material. Hyper-heuristics are high-level methodologies which operate on a search space of heuristics for solving complex problems. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heurist...

Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesm...

Genetic algorithms are adaptive methods which may be used as approximation heuristic for search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. A great problem in the use of genetic algorithms is the premature convergence, a premature stagnation of the search caused by the lack of diversity in...

In evolutionary algorithms, crossover is used to recombine two candidate solutions to yield a new solution which hopefully inherits good material from both. Hyper-heuristics are high-level search methodologies which operate on a search space of heuristics. Hyper-heuristics can be broadly split into two categories; heuristic selection and generation methodologies. Here we will investigate hyper-...

Evolutionary approaches to molecular docking typically use a realvalue encoding with standard genetic operators. Mutation is usually based on Gaussian and Cauchy distributions whereas for crossover no special considerations are made. The choice of operators is important for an efficient algorithm for this problem. We investigate their effect by performing a locality, heritability and heuristic ...

In order to tradeoff exploration/exploitation and inspired by cell genetic algorithm a cellshift crossover operator for evolutionary algorithm (EA) is proposed in this paper. The definition domain is divided into n-dimension cubic sub-domains (cell) and each individual locates at an ndimensional cube. Cell-shift crossover first exchanges the cell numbers of the crossover pair if they are in the...

The edge-set encoding is a direct tree encoding which applies search operators directly to trees represented as sets of edges. There are two variants of crossover operators for the edge-set encoding: With heuristics that consider the weights of the edges, or without heuristics. Due to a strong bias of the heuristic crossover operator towards the minimum spanning tree (MST) a population of solut...