The performance of a genetic algorithm (GA) is dependent on many factors: the type of crossover operator, the rate of crossover, the rate of mutation, population size, and the encoding used are just a few examples. Currently, GA practitioners pick and choose GA parameters empirically until they achieve adequate performance for a given problem. In this paper we have isolated one such parameter: ...

Multi-robot task allocation determines the task sequence and distribution for a group of robots in multi-robot systems, which is one of constrained combinatorial optimization problems and more complex in case of cooperative tasks because they introduce additional spatial and temporal constraints. To solve multi-robot task allocation problems with cooperative tasks efficiently, a subpopulation-b...

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 ...

This paper develops a new crossover operator, Sequential Constructive crossover (SCX), for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The sequential constructive crossover operator constructs an offspring from a pair of parents using better edges on the basis of their values that may be present in the parents' structure maintaining the seq...

Several studies on the variations of the crossover operator have shown that each of them presents speci£c properties that are interesting under particular circumstances. The advantages of each operator over others are often contradictory and the best operator depends on the problem being solved. This paper is based on the assumption that a combination of several crossover operators can take adv...

In this paper we study and compare the search properties of diierent crossover operators in genetic programming (GP) using probabilistic models and experiments to assess the amount of genetic material exchanged between the parents to generate the oospring. These operators are: standard crossover, one-point crossover and a new operator, uniform crossover. Our analysis suggests that standard cros...

In this paper novel hybrid genetic algorithms for Open--Shop Scheduling Problem (OSSP) are presented. Two greedy heuristics LPT-Task and LPT-Machine are proposed for decoding chromosomes represented by permutations with repetitions. For comparison the standard permutation representation of OSSP instances is used. The algorithms apply also efficient crossover operator LOX and mutation operators ...

Several recombination operators have been proposed in evolutionary computation. Standard procedures include one-point, two-point, and uniform crossover. Little attention, however, has been given to a recombination operator that preceded each of these, which was offered in 1957 by Alex Fraser. Fraser’s recombination assigns a variable probability for crossing over between two solutions at each l...

Formation of effective teams of experts has played a crucial role in successful projects especially in social networks. In this paper, a new particle swarm optimization (PSO) algorithm is proposed for solving a team formation optimization problem by minimizing the communication cost among experts. The proposed algorithm is called by improved particle optimization with new swap operator (IPSONSO...

The success of the application of evolutionary approaches depends, to a large extent, on problem representation and on the used genetic operators. In this paper we introduce a new graph based crossover operator and compare it with classical two-point crossover. The study was carried out using a theoretical hard problem known as Busy Beaver. This problem involves the search for the Turing Machin...