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Developing an Adaptation Process for Real-Coded Genetic Algorithms

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Developing an Adaptation Process for Real-Coded Genetic Algorithms Comput Syst Sci & Eng (2020) 1: 13–19 International Journal of © 2020 CRL Publishing Ltd Computer Systems Science & Engineering Developing an Adaptation Process for Real-Coded Genetic Algorithms ∗ Ridvan Saraço˘glu and Ahmet Fatih Kazankaya† Department of Electrical and Electronics Engineering, Van Yuzuncu Yil University, 65080, Turkey The genetic algorithm (GA) is a metaheuristic method which simulates the life cycle and the survival of the fittest in the nature for solving optimization problems. This study aimed to develop enhanced operation by modifying the current GA. This development process includes an adaptation method that contains certain developments and adds a new process to the classic algorithm. Individuals of a population will be trialed to adapt to the current solution of the problem by taking them separately for each generation. With this adaptation method, it is more likely to get better results in a shorter time. Experimental results show that this new process accelerated the algorithm and a certain solution has been reached in fewer generations. In addition, better solutions were achieved, especially for a certain number of generations. Keywords: Adaptive algorithms, Algorithm design and analysis, Genetic algorithms, Value coding genetic algorithms 1. INTRODUCTION of selection, evolution, crossover, and mutation stages [8, 9]. The population size entirely depends on the situation and The genetic algorithm (GA) is an optimization and searching the parameters. The first population is usually produced method, that belongs to an extended class of algorithms that indiscriminately and all possible gene variations are allowed are inspired by the natural selection process and are similar to in this scenario [10]. In each generation, a part of the current the evolutionary phase [1]. GA is widely used to produce high population is chosen to produce the next generation. Genes are quality genes for searching and optimization problems based lined up from low to high as for that their fitness values,and genes on biological facts such as selection, crossover and mutation. that are marked with higher fitness values are most likely to be In the GA, candidate individuals are expected to evolve the combed out depending on the parameters. Fitness functions that existing population to better genes for optimization problems evaluate the suitability of each gene are responsible for selecting [2]. Each candidate must have a number of features that can the best genes [4, 11]. be mutated and modified. Evolutionary stage begins with a Depending on the problem formula, the function is defined for population that is generated randomly, then the fitness value the simulated gene while also measuring the quality of the gene of every single individual is calculated and continued as a new [6, 12]. Next, new generation populations are produced from generation in each iteration [3]. Individuals with greater fitness the genes that were selected by the sequential combination of values are chosen from the current population and the genes of crossover and mutation [7]. While reaching reasonable results individuals arealtered to a new form [4]. The generation that for the problem being studied, it is very important to set the created currently is used in the next phases of the algorithm parameters of mutation, crossover factor and population size [5–7]. [11]. A pair of parents are selected from the population for new Once the fitness function has been identified, a gene genes to be produced [13]. The crossover method generates a population begins to be generated through repeated application child gene that shares most of the qualifications of the parents [4]. New parents are chosen in order to create new offspring and ∗ [email protected] the process sustains until a new solution size is found [14–16]. †[email protected] vol 35 no 1 January 2020 13 DEVELOPING AN ADAPTATION PROCESS FOR REAL-CODED GENETIC ALGORITHMS As a result of these processes, new generation chromosomes problem. In this chapter, the types of encoding techniques will are formed which are completely different from the first gener- be introduced. ation [17, 18]. It is expected that the average value of the fitness Binary encoding is one of the most popular techniques value of the population will increase by these processes because since the very first studies about GA were used with this the genes with the best fitness values and other genes with slightly encoding. In binary encoding, a gene has a combination of less fitness value are selected for the crossover process [19]. The a string of bits, 0 or 1. The binary encoding technique gives main reason to keep these genes with less fitness value is to genes many combinations. From another point of view, this increase the genetic diversity in both the genetic pool and future encoding technique is away being natural for many problems, generations [20, 21]. New offspring and some randomly selected and therefore, there should be some enlistments that need to be genes are subjected to low-probability mutations. This allows done after mutation and crossover phases [41]. some bits to be changed in their bit string [17, 18, 22]. The Second, permutation encoding, which can be used kind of mutation process is often used to retain variety in the population problems that include the traveling salesman problem or the and prevent early convergence [23–25]. The algorithm ends task ordering problem. In this method, every chromosome is when either the maximum population is generated or the aimed a combination of numbers. Permutation encoding has benefits fitness value of the population is reached [26]. if problem is an ordering one. This technique has mutation Attempts have been made for years ed to develop and expand and crossover corrections like the other techniques with the the GA by considering various factors to reach the desired difference of leaving the chromosome consistent [7]. solution in a shorter and more efficient way. These may vary Next, direct value encoding which can be used with real value depending on the purpose of the investigator; purposes such as; numbers. Using this technique brings some difficulties to the optimization design [24,27, 28], convenience function and range algorithm. In this type of encoding, every has a combination of [29], selection method [30, 31], population type and size [32], a string that can be almost everything according to the problem iteration number [33], crossing and mutation values [34], elite [42]. Value encoding is very effective for certain problems, and individual coefficient, stop criteria [25, 35] and gene pool [36] this encoding is often essential to develop a new mutation and can be important factors. crossover algorithm in order to make whole algorithm work In this study, a new adaptation process will be added between correctly [6]. the crossover and mutation process which are the two main Tree encoding is mainly used for progressing a program. In genetic stages of the GA. In this stage, firstly the best individual this technique\ every gene is a combination of a tree scheme of in the algorithm so far is determined as the donor individual. The articles. Tree encoding is useful for progressing or developing individual that has got the best fitness value among the population programs like LISP [33]. (donor) is determined and cloned on the randomly determined In the TGA, a population containing as many individuals ones. Thus, the goal of the algorithm is to reach generations as the determined size is created. Population individuals much earlier and with much better individuals. contain completely random values in the initial population size. The rest of the article details and gives specific information The number of genes of the current parameter size is created about the GA. In Chapter 2, selection, crossover, and mutation according to the feature of the problem. The fitness values can processes are discussed in detail. In Chapter 3, detailed be calculated according to random values assigned to genes and information about the process and the placement of the determined fitness function [6, 26]. adaptation phase is given. In Chapter 4, the experimental results that were obtained with the selected test functions and comments on these results are given. Finally, Chapter 5 contains the 2.2 Selection conclusions of the article. In principle, an individual population randomly selected from the search field offers candidate solutions to the problem. Then, 2. BACKGROUND these individuals will be crossed-over and mutated into new offspring. The new population is finally created by a procedure In this section, the basic elements of the GA which are the between parents and offspring. This process replicates until a repetitive main stages of the traditional genetic algorithm (TGA) aimed circumstanced is reached [43] such as selection, crossover and mutation are given [37]. After In Roulette Wheel Selection, the fitness function value is examining these phases, details about the proposed adaptation proportional to the probability of selection. In Rank Selection, phase to be added to the algorithm will be given. the order of their fitness value degree is proportional to the The GA is a method based on artificial intelligence, survival probability of selection. [44]. In the Tournament Selection of the fittest and evolutionary biology theory that used to find Method, a certain number of individuals are indiscriminately optimized solutions for problems and to make extensive searches picked and entered into the tournament according to their fitness among large data sets [6, 38–40]. values. For example, two individuals would be indiscriminately elected from the population as the 4th and 2nd individuals. One of these individuals will be selected when they are entered into 2.1 Encoding and Initial Population the tournament among themselves: such as the 2nd individual [45]. Encoding genes has great importance, when it is started to solve Those selection methods have different properties.
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