Abstract
Genetic algorithms (GAs) are powerful heuristic search techniques that are used successfully to solve problems for many different applications. Seeding the initial population is considered as the first step of the GAs.
 In this work, a new method is proposed, for the initial population seeding called the Multi Linear Regression Based Technique (MLRBT). That method divides a given large scale TSP problem into smaller sub-problems and the technique works frequently until the sub-problem size is very small, four cities or less. Experiments were carried out using the well-known Travelling Salesman Problem (TSP) instances and they showed promising results in improving the GAs' performance to solve the TSP.
Highlights
Genetic Algorithm (GA) is one of effective and robust machine learning algorithms [1]
The GAs start with a number of random solutions; this is the first phase in the GA
It can result in that these techniques are more beneficial than the random and the nearest neighbor (NN) techniques, where the successful performance has been achieved by the proposed solution and the achieved performance was close to the optimal solution
Summary
Genetic Algorithm (GA) is one of effective and robust machine learning algorithms [1]. Many studies were concerned with Genetic Algorithms (GA) and exploited its capabilities in designing smart systems and solving problems [2,3,4]. The genetic algorithms are concerned, in general, with how to produce new chromosomes (individuals) that possess certain features through recombination (crossover) and mutation operators [5,6]. The GAs start with a number of random solutions (initial population); this is the first phase in the GA. This phase generates a set of possible solutions randomly or by heuristic initialization. The initial population seeding phase is executed only once, it has an important role to improve the GA performance
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