This paper presents an evolutionary algorithm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called attributes. Some of these attributes are not relevant and needs to be eliminated. In classification procedure, each feature has an effect on the accuracy, cost and learning time of the classifier. So, t...

Abstract. Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) computational complexity (where is the number of objectives and is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algor...

In this paper, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) based approach is presented for distribution system reconfiguration. In contrast to the conventional GA based methods, the proposed approach does not require weighting factors for conversion of multi-objective function into an equivalent single objective function. In order to illustrate the performance of the proposed method,...

This paper presents the application of Nondominated Sorting Genetic Algorithm II (NSGA II) in solving multiple-objective signal timing optimization problem (MOSTOP). Some recent researches on intersection signal timing design optimization and multi-objective evolutionary algorithms are summarized. NSGA II, which can find more of the Pareto Frontiers and maintain the diversity of the population,...

A multi-objective vehicle path planning method has been proposed to optimize path length, path safety and path smoothness using the elitist non-dominated sorting genetic algorithm (NSGA-II). Four different path representation schemes that begin its coding from the start point and move one grid at a time towards the destination point are proposed. Minimization of traveled distance, maximization ...

Much attention has been paid to evolutionary multi-objective optimization approaches efficiently solve real-world engineering problems with multiple conflicting objectives. However, the loss of selection pressure and non-uniformity in distribution Pareto optimal solutions objective space can impede both dominance-based decomposition-based optimizers when solving many-objective problems. In this...

The university course timetabling problem is a typical combinatorial optimization problem. This paper tackles the multi-objective university course timetabling problem (MOUCTP) and proposes a guided search non-dominated sorting genetic algorithm to solve the MOUCTP. The proposed algorithm integrates a guided search technique, which uses a memory to store useful information extracted from previo...

Often, in many engineering applications it is required to find the best approximate solution of multi-objective optimization problems quick and with good accuracy. Multi-objective optimization problems are very common and important in real world applications, and as such many researchers are still working to devise various novel heuristic and mathematical approaches for their solution. Mathemat...

Surface grinding is the most common process used in the manufacturing sector to produce smooth finish on flat surfaces. Surface quality and metal removal rate are the two important performance characteristics to be considered in the grinding process. The economics of the machining process is affected by several factors such as abrasive wheel grade, wheel speed, depth of cut, table speed and mat...

Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(mN3) computational complexity (where m is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algo...