An Improved Genetic Algorithm with a New Initialization Mechanism Based on Regression Techniques

Abstract

Genetic algorithm (GA) is one of the well-known techniques from the area of evolutionary computation that plays a significant role in obtaining meaningful solutions to complex problems with large search space. GAs involve three fundamental operations after creating an initial population, namely selection, crossover, and mutation. The first task in GAs is to create an appropriate initial population. Traditionally GAs with randomly selected population is widely used as it is simple and efficient; however, the generated population may contain poor fitness. Low quality or poor fitness of individuals may lead to take long time to converge to an optimal (or near-optimal) solution. Therefore, the fitness or quality of initial population of individuals plays a significant role in determining an optimal or near-optimal solution. In this work, we propose a new method for the initial population seeding based on linear regression analysis of the problem tackled by the GA; in this paper, the traveling salesman problem (TSP). The proposed Regression-based technique divides a given large scale TSP problem into smaller sub-problems. This is done using the regression line and its perpendicular line, which allow for clustering the cities into four sub-problems repeatedly, the location of each city determines which category/cluster the city belongs to, the algorithm works repeatedly until the size of the subproblem becomes very small, four cities or less for instance, these cities are more likely neighboring each other, so connecting them to each other creates a somehow good solution to start with, this solution is mutated several times to form the initial population. We analyze the performance of the GA when using traditional population seeding techniques, such as the random and nearest neighbors, along with the proposed regression-based technique. The experiments are carried out using some of the well-known TSP instances obtained from the TSPLIB, which is the standard library for TSP problems. Quantitative analysis is carried out using the statistical test tools: analysis of variance (ANOVA), Duncan multiple range test (DMRT), and least significant difference (LSD). The experimental results show that the performance of the GA that uses the proposed regression-based technique for population seeding outperforms other GAs that uses traditional population seeding techniques such as the random and the nearest neighbor based techniques in terms of error rate, and average convergence.

Improving Initial Population for Genetic Algorithm using the Multi Linear Regression Based Technique (MLRBT)

“…To evaluate the proposed methods, he experiments on different TSP problems were conducted. Experiments include conducting the proposed method, which includes implementing the Multi Linear Regression Based Technique MLRBT together with the Regression based technique in [23], which was found superior to both the Random and the NN techniques. Table 1 shows the selected GA parameters.…”

“…Step 3: In this step, the diagram is divided into four sections as shown in into the four categories to obtain local optimal solutions [23].…”

“…The initial solution for a280 using MLRBT Figure 8 (A) The initial solution for rat195 using [23], (B) the initial solution for rat195 using MLRBT…”

An Iterated Hybrid Local Search Algorithm for Pick-and-Place Sequence Optimization

“…The path of instance 9 is shown in Figure 8. 235, 269, 205, 206, 267, 279, 202, 236, 199) 11 X 11 = (141, 156, 68, 21, 207, 180, 168, 110, 246, 111) 12 X 12 = (232, 181, 295, 257, 258, 229, 208, 248, 17, 262) 13 X 13 = (26,247,220,194,242,243,193,217,256,10) 14 197, 282, 281, 196, 218, 263, 225, 164, 161) 15 X 15 = (44, 95, 52, 119, 147, 162, 198, 27, 49, 71) 16 X 16 = (22, 7, 189, 123, 122, 83, 9, 216, 135, 138) 17 X 17 = (252, 11,255,191,227,250,249,221,182,254) 18 195,137,134,86,170,125,80,81,192,8)…”

“…Because of the complexity of the problem, heuristic approaches, such as meta-heuristic approaches and multi-stage heuristics strategy are widely used, while several exact methods can be found in previous research. Meta-heuristic approaches such as genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO) and ant colony optimization (ACO), which play an important role in solving complex problems [8][9][10][11][12], are altered to adapt to solving the problem efficiently [13][14][15][16]. Zhu and Zhang [17] added self-variation behavior to the frog leaping algorithm, which has good accuracy.…”