Abstract
Genetic algorithm (GA) is an artificial intelligence search method that uses the process of evolution and natural selection theory and is under the umbrella of evolutionary computing algorithm. It is an efficient tool for solving optimization problems. Integration among (GA) parameters is vital for successful (GA) search. Such parameters include mutation and crossover rates in addition to population that are important issues in (GA). However, each operator of GA has a special and different influence. The impact of these factors is influenced by their probabilities; it is difficult to predefine specific ratios for each parameter, particularly, mutation and crossover operators. This paper reviews various methods for choosing mutation and crossover ratios in GAs. Next, we define new deterministic control approaches for crossover and mutation rates, namely Dynamic Decreasing of high mutation ratio/dynamic increasing of low crossover ratio (DHM/ILC), and Dynamic Increasing of Low Mutation/Dynamic Decreasing of High Crossover (ILM/DHC). The dynamic nature of the proposed methods allows the ratios of both crossover and mutation operators to be changed linearly during the search progress, where (DHM/ILC) starts with 100% ratio for mutations, and 0% for crossovers. Both mutation and crossover ratios start to decrease and increase, respectively. By the end of the search process, the ratios will be 0% for mutations and 100% for crossovers. (ILM/DHC) worked the same but the other way around. The proposed approach was compared with two parameters tuning methods (predefined), namely fifty-fifty crossover/mutation ratios, and the most common approach that uses static ratios such as (0.03) mutation rates and (0.9) crossover rates. The experiments were conducted on ten Traveling Salesman Problems (TSP). The experiments showed the effectiveness of the proposed (DHM/ILC) when dealing with small population size, while the proposed (ILM/DHC) was found to be more effective when using large population size. In fact, both proposed dynamic methods outperformed the predefined methods compared in most cases tested.
Highlights
Genetic algorithms (GAs) are adaptive meta heuristic search algorithms which classified as an evolutionary computing algorithms, which use techniques inspired by natural evolution
We reviewed GA representations and parameter selection in GAs along with new deterministic approaches were proposed to change the crossover and mutation rates parameters dynamically
Based on several sets of experiments on real data from the TSPLIB, the Dynamic ILM/DHC was the best to operate with small population size, attaining the best solutions if compared with Fixed 50% for Mutation and Crossover Rates (FFMCR), 0.03MR0.9 crossover rate (CR) and DHM/increasing of low crossover (ILC) in small population (25, 50, 100)
Summary
Genetic algorithms (GAs) are adaptive meta heuristic search algorithms which classified as an evolutionary computing algorithms, which use techniques inspired by natural evolution. GAs have become a leading used approach to provide solutions to several complex optimization problems [4]. They are considered to be optimization tools [3,5]. The algorithm works to apply an evaluation function, which is provided by the programmer and depends on the problem type. This process is basically done to evaluate the individual’s goodness on how well they perform at a given problem task. This step continues until an optimal or the closest to optimal solution is found or some termination criteria are satisfied, though this depends mainly on the programmer in the first place [22]
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