Genetic algorithm

About: Genetic algorithm is a research topic. Over the lifetime, 67538 publications have been published within this topic receiving 1232117 citations. The topic is also known as: optimize problem & GA.

UAS Mission Path Planning System (MPPS) Using Hybrid-Game Coupled to Multi-Objective Optimizer

Abstract: This paper presents the application of advanced optimization techniques to unmanned aerial system mission path planning system (MPPS) using multi-objective evolutionary algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA nondominated sorting genetic algorithm II and a hybrid-game strategy are implemented to produce a set of optimal collision-free trajectories in a three-dimensional environment. The resulting trajectories on a three-dimensional terrain are collision-free and are represented by using Bezier spline curves from start position to target and then target to start position or different positions with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a hybrid-game strategy to a MOEA and for a MPPS.

Genetic Algorithm with Ensemble Learning for Detecting Community Structure in Complex Networks

Abstract: Community detection in complex networks is a topic of considerable recent interest within the scientific community. For dealing with the problem that genetic algorithm are hardly applied to community detection, we propose a genetic algorithm with ensemble learning (GAEL) for detecting community structure in complex networks. GAEL replaces its traditional crossover operator with a multi-individual crossover operator based on ensemble learning. Therefore, GAEL can avoid the problems that are brought by traditional crossover operator which is only able to mix string blocks of different individuals, but not able to recombine clustering contexts of different individuals into new better ones. In addition, the local search strategy, which makes mutated node be placed into the community where most of its neighbors are, is used in mutation operator. At last, a Markov random walk based method is used to initialize population in this paper, and it can provide us a population of accurate and diverse clustering solutions. Those diverse and accurate individuals are suitable for ensemble learning based multi-individual crossover operator. The proposed GAEL is tested on both computer-generated and real-world networks, and compared with current representative algorithms for community detection in complex networks. Experimental results demonstrate that GAEL is highly effective at discovering community structure.

Genetic algorithms with multiple objectives

Abstract: Most optimization strategies require that there be only one objective function. When multiple objectives exist for a design problem, the objectives must be combined into a single fitness value using weighting factors for the respective objectives. It can be very difficult to decide which weighting factors should be used.. A method of optimization for problems with more than one objective function which eliminates the need to weight the objectives is presented. This method uses a pareto fitness function which assigns a single fitness value to each of the designs based on all of the objective functions. A genetic algorithm is used to find the pareto optimal set of designs. The pareto optimal set contains all the designs which present the best combination of objective values regardless of weighting factors. The pareto set of designs can be presented to the decision-makers who ultimately decide the relative importance of the objectives. Two examples of this method are summarized. The first example examines the design and construction of a multi-story concrete building. The second example determines the optimal land zoning and street types for an area experiencing tremendous growth.

Effects of symbiotic evolution in genetic algorithms for job-shop scheduling

Abstract: In our research, we showed results of the comparative study on the effects of using several kinds of scheduling evaluation criteria as the fitness function of a genetic algorithm for job shop scheduling. From these results, we obtained that the idle time criterion sometimes can provide a good makespan-minimizing schedule for a job-shop scheduling problem. In this paper, according to the above results, we propose a symbiotic genetic algorithm. The symbiotic genetic algorithm is structured with two kinds of evolution processes: a co-evolution process in which both makespan and idle time schedule criteria are employed as the fitness functions into the operation-based genetic algorithm for job-shop scheduling; and a sub-evolution process in which the total job waiting time schedule criterion is used as the fitness to provide high diversity for chromosome population. The symbiotic genetic algorithm is tested on famous benchmark job-shop scheduling problems. Further, we introduce the concept of software system for job-shop scheduling based on the proposed method.

The Multi-Objective Genetic Algorithm Applied to Benchmark Problems An Analysis

Abstract: The multiobjective genetic algorithm (MOGA) has been applied to various real-world problems in a variety of fields, most prominently in control systems engineering, with considerable success. However, a recent empirical analysis of multi-objective evolutionary algorithms (MOEA's) has suggested that a MOGA-based algorithm performed poorly across a diverse set of two-objective test problems. In this report, it is shown that a conventional MOGA with standard settings can provide improved performance, but this still compares unfavourably to the best-performing contemporary MOEA, the Strength Pareto Evolutionary Algorithm (SPEA). The importance of the MOEA, as a framework is stressed and consequently, a real-coded MOGA for real-parameter multi-criterion problems is developed using modern gudelines for the design of evolutionary algorithms. This MOGA is shown to outperform the "best" MOEA, rather that a considered implementation of the methodology is required in order to reap full rewards. This study also questions the effectiveness of the traditional fitness sharing method of niching, with respect to the current set of multiobjective benchmark problems.