In this paper we explore the influence of the dynamics of evolution on coding structures of sequences. We show that, in systems with crossover, high mutation rates cause the rnoq conserved subsequences to he preferentially used as recognition sites for newly evolving sequences. In other words: “multiple coding” evolves in these systems. Multiple coding often does not increase. the fitness of th...

In real-world production processes, the same enterprise often has multiple factories or one factory lines, and objectives need to be considered in process. A dual-population genetic algorithm with Q-learning is proposed minimize maximum completion time number of tardy jobs for distributed hybrid flow shop scheduling problems, which have some symmetries machines. Multiple crossover mutation oper...

This paper is concerned with the determination of optimal sensor locations for structural modal identification in a strap-braced cold formed steel frame based on an improved genetic algorithm (IGA). Six different optimal sensor placement performance indices have been taken as the fitness functions two based on modal assurance criterion (MAC), two based on maximization of the determinant of a Fi...

In modern manufacturing systems like multi-resource constrained project scheduling problem with the multiple modes (mcPSP-mM) is complicated because of the complex interrelationships between the units of the different stages. In this paper, we develop an adaptive genetic algorithm (aGA) to solve the mcPSP-mM which is a well known NP-hard problem. A new aGA algorithm approach for solving these m...

This paper proposes a generalized Hybrid Real-coded Quantum Evolutionary Algorithm (HRCQEA) for optimizing complex functions as well as combinatorial optimization. The main idea of HRCQEA is to devise a new technique for mutation and crossover operators. Using the evolutionary equation of PSO a Single-Multiple gene Mutation (SMM) is designed and the concept of Arithmetic Crossover (AC) is used ...

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 ...

The Evolutionary Algorithm is a population-based metaheuristic optimization algorithm. The EA employs mutation, crossover and selection operators inspired by biological evolution. It is commonly applied to find exact or approximate solutions to combinatorial search and optimization problems. This dissertation describes a series of theoretical and experimental studies on a variety of evolutionar...

Beginning with the early days of the genetic algorithm and the schema theorem it has often been argued that the crossover operator is the more important genetic operator. The early Royal Road functions were put forth as an example where crossover would excel, yet mutation based EAs were subsequently shown to experimentally outperform GAs with crossover on these functions. Recently several new R...

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: ...

The purpose of this paper is to discuss the implementation and performance of two genetic operators specifically tuned to solve the Travelling Salesman Problem. The two operators discussed are the Greedy Knot-Cracker crossover operator and a modified version of the 3-opt mutation operator. Specifically the paper shall discuss the motivation for choosing these operators, their implementation, th...