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The isin-DANTE method is an hybrid meta-heuristic. In combines the evolutionary ant colony optimization (ACO) algorithms with a limited depth search. This depth search is based in the pheromone trails used by the ACO, which allows it to be oriented to the more promising areas of the search space. Some results are presented for the multiple objective k-degree spanning trees problem, proving the effectiveness...
Common evolutionary approaches to protein-ligand docking optimization use mutation operators based on Gaussian and Cauchy distributions, with local search hybrids. The choice of a local search method is important for an efficient algorithm. We investigate the impact of local search with mutation operators by performing a locality analysis. High locality means that small variations in the genotype...
The harmony search (HS) method is an emerging meta-heuristic optimization algorithm. In this paper, we propose two modified HS methods to deal with the uni-modal and multi-modal optimization problems. The first modified HS method is based on the fusion of the HS and differential evolution (DE) technique, namely, HS-DE. The DE is employed here to optimize the members of the HS memory. The second modified...
Hybridizing of evolutionary algorithms (EA) by means of local search has shown considerable performance improvement in single-objective optimization (SOO) field. The fine search in the neighborhood of the EA individuals (solutions) allows a fine exploration of the solution space. This paper investigates the application and the evaluation of the hybridizing mechanism of the EAs in the multi-objective...
This paper presents a hybrid efficient method namely hybrid immune algorithm (HIA) based on artificial immune algorithm (AIA) and bacterial optimization for clustering problems. Four local searches on the basis of heuristic rules for the given clustering problem are designed and applied. This proposed method is implemented and tested on two real datasets. Further, its performance is compared with...
In this paper, we are studying a generalized version of the strength Pareto evolutionary algorithm 2 (SPEA2). By replacing the algorithmic-internal role of the Pareto-dominance relation with a different, not necessarily transitive relation, the algorithm can become capable to search for the maximum set of the replacing relation. Thus, the SPEA2 algorithm can also become capable of on-line decision...
In this paper, we highlight the use of synthetic data sets to analyze learners behavior under bounded complexity. We propose a method to generate synthetic data sets with a specific complexity, based on the length of the class boundary. We design a genetic algorithm as a search technique and find it useful to obtain class labels according to the desired complexity. The results show the suitability...
This paper proposes the application of a novel bio-inspired algorithm as a search engine to the feature subset selection problem. We may interpret our algorithm as an estimation of distribution algorithm that adopts an artificial immune system to implement the search process in the space of all features and a Bayesian network to implement the probabilistic model of the promising solutions. The characteristics...
The main objective of this work is to automatically design neural network models with sigmoidal basis units for classification tasks, so that classifiers are obtained in the most balanced way possible in terms of CCR and sensitivity (given by the lowest percentage of examples correctly predicted to belong to each class). We present a memetic Pareto evolutionary NSGA2 (MPENSGA2) approach based on the...
We propose a self-adaptive hybrid evolutionary algorithm for the optimization of Morse clusters. The approach relies on a two-phase local optimization method to efficiently guide search. Individuals encode its own penalty settings and the algorithm evolves them simultaneously with the search for low energy clusters. Results show that the approach is efficient, as it is able to discover all optimal...
This paper presents the implementation of a constraint-handling technique within the electromagnetism-like algorithm devised by Birbil and Fang, for solving real-world engineering design problems. A derivative-free elite-based descent search scheme is also included in the final stage of each iteration to accelerate convergence and improve accuracy. Numerical experiments with a set of engineering problems...
Haplotype inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This piece of information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as...
In this paper we present a study of an Ant Colony System (ACS) for the two-dimensional strip packing problem. In our computational study, we emphasize the influence of incorporating a simple optimization method at each cycle of the ACS. In this hybrid approach, local optimization is applied to a subset of the newly generated solutions to move them to a local optimum. We show that our ACS algorithm,...
Many specific algorithms and metaheuristics have been proposed for solving the DNA fragment assembly problem, but new algorithms with more capacity for solving this problem are necessary. The fragment assembly problem consists in building the DNA sequence from several hundreds (or even, thousands) of fragments obtained by biologists in the laboratory. This is an important task in any genome project...
This article considers the bi-objective job shop scheduling problem in which the make span and the total tardiness of jobs are minimized. In order to find a set of dominant solutions, that is, an approximation of the Pareto optimal solutions, we propose three versions of a genetic algorithm with techniques like hybridization with local search, path relinking and elitism. The three versions of the...
This study considers a single machine scheduling problem with the objective of minimizing the total weighted tardiness of the jobs. This problem is one of the most famous problems in single machine scheduling theory and it is NP-hard. In this paper, we propose a hybrid heuristic which combines GRASP with Path Relinking to find good quality solutions for the considered problem. The performance of the...
Machine scheduling is a critical problem in industries where products are custom-designed. The wide range of products, the lack of previous experiences in manufacturing, and the several conflicting criteria used to evaluate the quality of the schedules define a huge search space. Furthermore, production complexity and human influence in each manufacturing step make time estimations difficult to obtain...
This paper proposes an algorithm to solve multi-objective problems by Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) which uses an external population, called pareto vector set P, in genetic operators. RasID is an optimization algorithm, which is good at finding local optima, but its diversified search isn't so efficient. To increase its...
This paper describes a method to automatically tuning artificial neural networks parameters for a specific problem using an evolutionary algorithm. The method employs an evolutionary search to perform simultaneous tuning of initial weights, transfer functions, architectures and learning rules (learning algorithms parameters). Experiments were performed and the results demonstrate that the method in...
The goal of this paper is twofold. First, we want to make a study about how evolutionary computation techniques can efficiently solve the radio network design problem. For this goal we test several evolutionary computation techniques within the OPLINK experimental framework and compare them. Second, we propose a clustering approach and a 2-OPT in order to improve the results obtained by the evolutionary...
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