International Journal of Applied Metaheuristic Computing
About: International Journal of Applied Metaheuristic Computing is an academic journal published by IGI Global. The journal publishes majorly in the area(s): Metaheuristic & Computer science. It has an ISSN identifier of 1947-8283. Over the lifetime, 311 publications have been published receiving 2264 citations. The journal is also known as: IJAMC.
Topics: Metaheuristic, Computer science, Particle swarm optimization, Genetic algorithm, Optimization problem
TL;DR: A review of the developments of Harmony Search during the past decade is given and a rigorous analysis of this approach is performed to compare it to the well-known search heuristic called Evolution Strategies.
Abstract: In recent years a lot of novel (mostly naturally inspired) search heuristics have been proposed. Among those approaches is Harmony Search. After its introduction in 2000, positive results and improvements over existing approaches have been reported. In this paper, the authors give a review of the developments of Harmony Search during the past decade and perform a rigorous analysis of this approach. This paper compares Harmony Search to the well-known search heuristic called Evolution Strategies. Harmony Search is a special case of Evolution Strategies in which the authors give compelling evidence for the thesis that research in Harmony is fundamentally misguided. The overarching question is how such a method could be inaccurately portrayed as a significant innovation without confronting a respectable challenge of its content or credentials. The authors examine possible answers to this question, and implications for evaluating other procedures by disclosing the way in which limitations of the method have been systematically overlooked.
TL;DR: This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study.
Abstract: Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution and multiple perturbation low level heuristics. An initially generated complete solution goes through two successive processes heuristic selection and move acceptance until a set of termination criteria is satisfied. A motivating goal of hyper-heuristic research is to create automated techniques that are applicable to a wide range of problems with different characteristics. Some previous studies show that different combinations of heuristic selection and move acceptance as hyper-heuristic components might yield different performances. This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study.
TL;DR: A survey on the application of Evolutionary Algorithms to Instance Selection and Generation process will cover approaches applied to the enhancement of the nearest neighbor rule, as well as other approaches focused on the improvement of the models extracted by some well-known data mining algorithms.
Abstract: The use of Evolutionary Algorithms to perform data reduction tasks has become an effective approach to improve the performance of data mining algorithms. Many proposals in the literature have shown that Evolutionary Algorithms obtain excellent results in their application as Instance Selection and Instance Generation procedures. The purpose of this paper is to present a survey on the application of Evolutionary Algorithms to Instance Selection and Generation process. It will cover approaches applied to the enhancement of the nearest neighbor rule, as well as other approaches focused on the improvement of the models extracted by some well-known data mining algorithms. Furthermore, some proposals developed to tackle two emerging problems in data mining, Scaling Up and Imbalance Data Sets, also are reviewed.
TL;DR: A novel particle swarm optimization based meta-heuristic algorithm is presented to solve multi-objective combinatorial optimization problems and is compared with other evolutionary strategy like SPEA and NSGA-II on pseudo-Boolean discrete problems and multi- objective 0/1 knapsack problem.
Abstract: The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-heuristic approaches are found suitable. In this paper, a novel particle swarm optimization based meta-heuristic algorithm is presented to solve multi-objective combinatorial optimization problems. Here a mapping method is considered to convert the binary and discrete values (solution encoded as particles) to a continuous domain and update it using the velocity and position update equation of particle swarm optimization to find new set of solutions in continuous domain and demap it to discrete values. The performance of the algorithm is compared with other evolutionary strategy like SPEA and NSGA-II on pseudo-Boolean discrete problems and multi-objective 0/1 knapsack problem. The experimental results confirmed the better performance of combinatorial particle swarm optimization algorithm.
TL;DR: The results confirm that the developed MARS and LSSVM models are robust for prediction of su, based Cone Penetration Test CPT data.
Abstract: This study adopts Multivariate Adaptive Regression Spline MARS and Least Square Support Vector Machine LSSVM for prediction of undrained shear strength su of clay, based Cone Penetration Test CPT data. Corrected cone resistance qt, vertical total stress sv, hydrostatic pore pressure u0, pore water pressure at the cone tip u1, and pore water pressure just above the cone base u2 are used as input parameters for building the MARS and LSSVM models. The developed MARS and LSSVM models give simple equations for prediction of su. A comparative study between MARS and LSSSM is presented. The results confirm that the developed MARS and LSSVM models are robust for prediction of su.