Deciding on an appropriate population size for a given GA application can often be critical to the algorithm's success. Too small, and the GA can fall victim to sampling error, affecting the efficacy of its search. Too large, and the GA wastes computational resources. Although advice exists for sizing GA populations, much of this advice involves theoretical aspects that are not accessible to th...

In recent years, DNA-GA algorithm is drawing attention from scholars. The algorithm combines the DNA encoding and Genetic Algorithm, which solves the premature convergence of genetic algorithms, the weak local search capability and binary Hamming cliff problems effectively. How to design a more effective way to improve the performance of DNA-GA algorithm is more worth studying.As is known to al...

A hybrid genetic scheduling strategy (H-GA) is described in this article, H-GA combines with grouping and load balancing strategy based on traditional genetic algorithm (GA). First, tasks are divided into several different subgroups by task granularity. Then, task subgroup which is selected by granularity from big to small is used to schedule by the genetic algorithm, and during scheduling, the...

This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA) and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant comp...

Mixed model two-sided assembly lines (MM2SAL) are applied to assemble large product models, which is produced in high-volume. So, the sequence planning of products to reduce cost and increase productivity in this kind of lines is imperative. The presented problem is tackled in two steps. In step 1, a framework is developed to select and prioritize customer orders under the finite capacity of th...

this study is focused on developing an integrated optimization-simulation based genetic algorithm model (iosga) to develop the operational policies for a multi-purpose reservoir system. the objective function of the optimization model is considered to be a linear function of reliability (rel), resiliency (res), and vulnerability (vul) of the river-reservoir system. genetic algorithm (ga) is emp...

this paper aims to investigate the integrated production/distribution and inventory planning for perishable products with fixed life time in the constant condition of storage throughout a two-echelon supply chain by integrating producers and distributors. this problem arises from real environment in which multi-plant with multi-function lines produce multi-perishable products with fixed life ti...

In this paper, we present an application of evolved neural networks using a real coded genetic algorithm for simulations of monthly groundwater levels in a coastal aquifer located in the Shabestar Plain, Iran. After initializing the model with groundwater elevations observed at a given time, the developed hybrid genetic algorithm-back propagation (GA-BP) should be able to reproduce groundwater ...

multi-criteria shortest path problems (mspp) are called as np-hard. for mspps, a unique solution for optimizing all the criteria simultaneously will rarely exist in reality. algorithmic and approximation schemes are available to solve these problems; however, the complexity of these approaches often prohibits their implementation on real-world applications. this paper describes the development ...

This paper aims at optimizing the parameters involved in stress analysis of perforated plates, in order to achieve the least amount of stress around the square-shaped holes located in a finite isotropic plate using metaheuristic optimization algorithms. Metaheuristics may be classified into three main classes: evolutionary, physics-based, and swarm intelligence algorithms. This research uses Ge...