The Novel Approach of Adaptive Twin Probability for Genetic Algorithm

The performance of GA is measured and analyzed in terms of its performance parameters against variations in its genetic operators and associated parameters. Since last four decades huge numbers of researchers have been working on the performance of GA and its enhancement. This earlier research work on analyzing the performance of GA enforces the need to further investigate the exploration and exploitation characteristics and observe its impact on the behavior and overall performance of GA. This paper introduces the novel approach of adaptive twin probability associated with the advanced twin operator that enhances the performance of GA. The design of the advanced twin operator is extrapolated from the twin offspring birth due to single ovulation in natural genetic systems as mentioned in the earlier works. The twin probability of this operator is adaptively varied based on the fitness of best individual thereby relieving the GA user from statically defining its value. This novel approach of adaptive twin probability is experimented and tested on the standard benchmark optimization test functions. The experimental results show the increased accuracy in terms of the best individual and reduced convergence time.Comment: 7 pages, International Journal of Advanced Studies in Computer Science and Engineering (IJASCSE), Volume 2, Special Issue 2, 201

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selection of appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer ReviewedPostprint (published version

Layout Optimization of Microsatellite Components Using Genetic Algorithm

The placement of satellite components usually belongs to non-deterministic polynomial-time hard (NP-hard) problems that in terms of computational complexity is very difficult to solve. This problem is normally known as layout optimization problem (LOP). In this study the layout of microsatellite components has to meet the requirements set by mission payloads, launcher and spacecraft attitude control. The novel scheme is to find the various possibilities of optimal layout using genetic algorithms combined with order-based positioning technique (OPT). Each component has a given index and then placed in a container based on specific order of placements in accordance with a bottom-left (BL) algorithm that is already established. The placement order is generated by the genetic algorithm which explore various possibilities to obtain a sequence that brings the best solution

Evolutionary Computation in System Identification: Review and Recommendations

Two of the steps in system identification are model structure selection and parameter estimation. In model structure selection, several model structures are evaluated and selected. Because the evaluation of all possible model structures during selection and estimation of the parameters requires a lot of time, a rigorous method in which these tasks can be simplified is usually preferred. This paper reviews cumulatively some of the methods that have been tried since the past 40 years. Among the methods, evolutionary computation is known to be the most recent one and hereby being reviewed in more detail, including what advantages the method contains and how it is specifically implemented. At the end of the paper, some recommendations are provided on how evolutionary computation can be utilized in a more effective way. In short, these are by modifying the search strategy and simplifying the procedure based on problem a priori knowledge

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selectionof appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer Reviewe

Parameter Optimization of Genetic Algorithm Utilizing Taguchi Design for Gliding Trajectory Optimization of Missile

The present study aims to establish a genetic algorithm (GA) method to optimize gliding trajectory of a missile. The trajectory is optimized by discretizing the angle of attack (AOA) and solving optimal control problem to achieve maximum gliding range. GA is employed to resolve the optimal control problem to achieve optimized AOA. A Taguchi’s design of experiments was proposed contrary to full factorial method to ascertain the GA parameters. The experiments have been designed as per Taguchi’s design of experiments using L27 orthogonal array. Systematic reasoning ability of Taguchi method is exploited to obtain better selection, crossover and mutation operations and consequently, enhance the performance of GA for gliding trajectory optimization. The effects of GA parameters on gliding trajectory optimization are studied and analysis of variance (ANOVA) is carried out to evaluate significance factors on the results. Crossover function and population size are observed as highly impacting parameter in missile trajectory optimization accompanied by selection method, crossover fraction, mutation rate and number of generations. Artificial neural network (ANN) method was also applied to predict the significance of GA parameters. The results show that the gliding range is maximized after GA parameter tuning. Simulation results also portrayed that with optimal AOA, gliding distance of missile is improved compared to earlier one. The numerical simulation shows the efficiency of proposed procedure via various test scenarios

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selectionof appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem

Statistical optimisation and tuning of GA factors.

This paper presents a practical methodology of improving the efficiency of Genetic Algorithms through tuning the factors significantly affecting GA performance. This methodology is based on the methods of statistical inference and has been successfully applied to both binary- and integer-encoded Genetic Algorithms that search for good chemotherapeutic schedules

Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances

This work presents a statistically principled method for estimating the required number of instances in the experimental comparison of multiple algorithms on a given problem class of interest. This approach generalises earlier results by allowing researchers to design experiments based on the desired best, worst, mean or median-case statistical power to detect differences between algorithms larger than a certain threshold. Holm’s step-down procedure is used to maintain the overall significance level controlled at desired levels, without resulting in overly conservative experiments. This paper also presents an approach for sampling each algorithm on each instance, based on optimal sample size ratios that minimise the total required number of runs subject to a desired accuracy in the estimation of paired differences. A case study investigating the effect of 21 variants of a custom-tailored Simulated Annealing for a class of scheduling problems is used to illustrate the application of the proposed methods for sample size calculations in the experimental comparison of algorithms