Diseño de algoritmos evolutivos híbridos optimizados para biclustering : Línea de investigación

El objetivo general de esta línea de investigación consiste en diseñar nuevas técnicas computacionales que ayuden a descubrir potenciales conexiones entre datos presentados en forma de matriz pertenecientes a distintos campos de aplicación. Más específicamente, se planea desarrollar una estrategia evolutiva hibridada con búsqueda local especialmente diseñada para bilcustering de datos. En tal sentido, se busca desarrollar una herramienta que pueda asistir a investigadores de distintas disciplinas en la inferencia de relaciones entre datos procedentes de grandes volúmenes de información.Eje: Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informática (RedUNCI

A Novel Memetic Feature Selection Algorithm

Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity. In feature selection algorithms search strategies are key aspects. Since feature selection is an NP-Hard problem; therefore heuristic algorithms have been studied to solve this problem. In this paper, we have proposed a method based on memetic algorithm to find an efficient feature subset for a classification problem. It incorporates a filter method in the genetic algorithm to improve classification performance and accelerates the search in identifying core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the multivariate feature information. Empirical study on commonly data sets of the university of California, Irvine shows that the proposed method outperforms existing methods

A Multi-Objective Memetic Optimization Method for Power Network Cascading Failures Protection

International audienceReliable and safe power grid operation requires the anticipation of cascading failures and the establishment of appropriate protection plans for their management. In this paper, we address this latter problem by line switching and propose a multi-objective memetic algorithm (MOMA), which combines the binary differential evolution algorithm with the non-dominated sorting mechanism and the Lamarckian local search. The 380 kV Italian power transmission network is used as a realistic test case

Comparison of Multiobjective Memetic Algorithms on 0/1 Knapsack Problems

The paper compares two well-known multiobjective memetic algorithms through computational experiments on 0/1 knapsack problems. The two algorithms are MOGLS (multiple objective genetic local search) of Jaszkiewicz and M-PAES (memetic Pareto archived evolution strategy) of Knowles & Corne. It is shown that the MOGLS with a sophisticated repair algorithm based on the current weight vector in the scalar fitness function has much higher search ability than the M-PAES with a simple repair algorithm. When they use the same simple repair algorithm, the M-PAES performs better overall. It is also shown that the diversity of non-dominated solutions obtained by the MPAES is small in comparison with the MOGLS. For improving the performance of the M-PAES, we examine the use of the scalar fitness function with a random weight vector in the selection procedure of parent solutions

Mixed-Integer Constrained Optimization Based on Memetic Algorithm

Evolutionary algorithms (EAs) are population-based global search methods. They have been successfully applied tomany complex optimization problems. However, EAs are frequently incapable of finding a convergence solution indefault of local search mechanisms. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators withlocal search methods. With global exploration and local exploitation in search space, MAs are capable of obtainingmore high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-basedsearch algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, amemetic algorithm based on MIHDE is developed for solving mixed-integer optimization problems. However, most ofreal-world mixed-integer optimization problems frequently consist of equality and/or inequality constraints. In order toeffectively handle constraints, an evolutionary Lagrange method based on memetic algorithm is developed to solvethe mixed-integer constrained optimization problems. The proposed algorithm is implemented and tested on twobenchmark mixed-integer constrained optimization problems. Experimental results show that the proposed algorithmcan find better optimal solutions compared with some other search algorithms. Therefore, it implies that the proposedmemetic algorithm is a good approach to mixed-integer optimization problems

An Automated Parameter Search Method for Vehicle Transmission System

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 이창건.In the vehicle industry, shift quality is a major factor to evaluate the performance of a transmission unit. Following by the electric control units like ECU (Engine Control Unit) and TCU (Transmission Control Unit) took important role in the vehicle transmission, the computational parameters in the control units also took the key role to determine a shift quality. To find a set of optimal parameters, trial-and-error technique is most famous in the field. However the technique requires human experience for efficient and qualified parameter search. To generalize the quality and search time, there is a certain demand on the automation of parameter search. The studies for automated calibration of automatic transmission system for shift quality have been placed in the automobile field. Actual vehicle and Dynamometer are used to automate the system, and they are based on the technique called Design of Experiment. Since the vehicle transmission systems are complex, exhaustive search technique is not proper to solve the problem. Design of Experiment limited parameters to handle only key factors. However the technique still requires human experience at the design level. In this thesis, we propose a genetic algorithm based transmission parameter search method for a vehicle transmission system. In the pre-process phase, learn each parameters effect on the target shift quality to avoid the dependency on human experience. Global search with genetic algorithm approach and local optimization is implemented. Simulink based vehicle simulator is used for experiment.1. Introduction 1 2. Related Work 5 3. Background and Problem Description 7 3.1 Simulink Vehicle Simulator 7 3.2 Clutch System 7 3.3 Genetic Algorithm 8 3.4 Design of Experiment 9 3.5 Problem Description 10 4. Proposed Solution 14 4.1 Existing Solution 14 4.2 Our Approach 15 5. Experiment 23 6. Conclusion and Future Work 27 7. References 28Maste