Genetic algorithm and pure random search for exosensor distribution optimisation

Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined with our flexible adaptive searching strategy. The efficiency of the algorithm is investigated by computational experiments.

Section: Literature Reviewmentioning

confidence: 99%

An Adaptive Evolutionary Algorithm for Traveling Salesman Problem with Precedence Constraints

Sung

Jeong

2014

“…Examples include applying evolution strategy to the problem of optimal multilayer coating design [ 11 ] and to optimize chemical engineering plants [ 12 ]. Other cases include an ant colony system for optimizing electrical power distribution networks [ 13 ], a genetic algorithm to optimize the design of antenna [ 14 ], to optimize the deployment of patrol manpower [ 15 ], and to optimize exosensor distribution for smart home systems [ 16 ]. Yokota et al proposed a genetic algorithm to solve nonlinear mixed integer programming [ 17 ] and there are many other algorithms created for solving the general MILP [ 18 – 20 ].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Evolutionary Algorithm for Wheat Blending Problem

Bonyadi

Michalewicz

et al. 2014

This paper presents a hybrid evolutionary algorithm to deal with the wheat blending problem. The unique constraints of this problem make many existing algorithms fail: either they do not generate acceptable results or they are not able to complete optimization within the required time. The proposed algorithm starts with a filtering process that follows predefined rules to reduce the search space. Then the linear-relaxed version of the problem is solved using a standard linear programming algorithm. The result is used in conjunction with a solution generated by a heuristic method to generate an initial solution. After that, a hybrid of an evolutionary algorithm, a heuristic method, and a linear programming solver is used to improve the quality of the solution. A local search based posttuning method is also incorporated into the algorithm. The proposed algorithm has been tested on artificial test cases and also real data from past years. Results show that the algorithm is able to find quality results in all cases and outperforms the existing method in terms of both quality and speed.

“…A cross-study comparison of classification methods including ANN and SVM for predicting metastasis in breast cancer is presented by Burton et al [ 43 ]. Other approaches using evolutionary computation techniques are presented in [ 44 , 45 ].…”

Section: State Of the Artmentioning

confidence: 99%

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

Fernández-Lozano

Canto

Gestal

et al. 2013

Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected.