32,202 research outputs found
A Computational Study of Genetic Crossover Operators for Multi-Objective Vehicle Routing Problem with Soft Time Windows
The article describes an investigation of the effectiveness of genetic
algorithms for multi-objective combinatorial optimization (MOCO) by presenting
an application for the vehicle routing problem with soft time windows. The work
is motivated by the question, if and how the problem structure influences the
effectiveness of different configurations of the genetic algorithm.
Computational results are presented for different classes of vehicle routing
problems, varying in their coverage with time windows, time window size,
distribution and number of customers. The results are compared with a simple,
but effective local search approach for multi-objective combinatorial
optimization problems
Feature selection for high dimensional regression using local search and statistical criteria
International audienceGenomic selection is a genetic evaluation of animals from their DNA, based on a huge number of markers covering the whole genome. It requires advanced approaches and in particular feature selection methods. Feature selection is a combinatorial problem that may be addressed by combinatorial optimization methods. We propose to combine an iterated local search (ILS) with a statistical evaluation of a multivariate regression and we compared three criteria in order to analyse their impact on the performance of the local search
Superior Exploration-Exploitation Balance with Quantum-Inspired Hadamard Walks
This paper extends the analogies employed in the development of
quantum-inspired evolutionary algorithms by proposing quantum-inspired Hadamard
walks, called QHW. A novel quantum-inspired evolutionary algorithm, called
HQEA, for solving combinatorial optimization problems, is also proposed. The
novelty of HQEA lies in it's incorporation of QHW Remote Search and QHW Local
Search - the quantum equivalents of classical mutation and local search, that
this paper defines. The intuitive reasoning behind this approach, and the
exploration-exploitation balance thus occurring is explained. From the results
of the experiments carried out on the 0,1-knapsack problem, HQEA performs
significantly better than a conventional genetic algorithm, CGA, and two
quantum-inspired evolutionary algorithms - QEA and NQEA, in terms of
convergence speed and accuracy.Comment: 2 pages, 2 figures, 1 table, late-breakin
A Memetic Algorithm for Logic Circuit Design
Memetic Algorithms (MAs) have shown to be very effective in solving many hard combinatorial optimization problems. In this perspective, this paper presents a MA for combinational logic circuits synthesis. The proposed MA combines a Genetic Algorithm (GA) for digital circuit design with the gate type local search (GTLS). The combination of a global and a local search is a strategy used by many successful hybrid optimization approaches. The main idea is to apply a local refinement to an Evolutionary Algorithm (EA) in order to improve the fitness of the individuals in the population. The obtained results indicate that the MA reduces the number of generations required to reach the solutions and its standard deviation while improves the final fitness function.N/
Global Optimization Using Local Search Approach for Course Scheduling Problem
Course scheduling problem is a combinatorial optimization problem which is defined over a finite discrete problem whose candidate solution structure is expressed as a finite sequence of course events scheduled in available time and space resources. This problem is considered as non-deterministic polynomial complete problem which is hard to solve. Many solution methods have been studied in the past for solving the course scheduling problem, namely from the most traditional approach such as graph coloring technique; the local search family such as hill-climbing search, taboo search, and simulated annealing technique; and various population-based metaheuristic methods such as evolutionary algorithm, genetic algorithm, and swarm optimization. This article will discuss these various probabilistic optimization methods in order to gain the global optimal solution. Furthermore, inclusion of a local search in the population-based algorithm to improve the global solution will be explained rigorously
A Memetic Algorithm for Logic Circuit Design
Memetic Algorithms (MAs) have shown to be very effective in solving many hard combinatorial optimization problems. In this perspective, this paper presents a MA for combinational logic circuits synthesis. The proposed MA combines a Genetic Algorithm (GA) for digital circuit design with the gate type local search (GTLS). The combination of a global and a local search is a strategy used by many successful hybrid optimization approaches. The main idea is to apply a local refinement to an Evolutionary Algorithm (EA) in order to improve the fitness of the individuals in the population. The obtained results indicate that the MA reduces the number of generations required to reach the solutions and its standard deviation while improves the final fitness function.N/
Automatic Class Timetable Generation using a Hybrid Genetic and Tabu Algorithm
Timetable generation is a combinatorial optimization problem. Meta Heuristic methods and Evolutionary Algorithms have given the best results when it comes to solving the problem of timetable generation. In our paper the problem of timetable generation for the Computer Science and Engineering Dept. of BMS College of Engineering is solved with the help of Genetic Algorithm and Tabu Search which belong to the class of Evolutionary Algorithms and Meta – Heuristics respectively. Genetic Algorithms help in finding multiple optimal solutions in one iteration but they can get stuck at local optima. This can be avoided by using Tabu Search procedure.
DOI: 10.17762/ijritcc2321-8169.150510
Ant colony optimisation and local search for bin-packing and cutting stock problems
The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO
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