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Genetic Algorithms (GAs) are powerful general-purpose optimization search algorithms based upon the principles of evolution observed in nature. Mutation operator is one of the GA operators that used to produce new chromosomes or modify some features of it depending on some small probability value. The objective of this operator is to prevent falling of all solutions in population into a local optimum...
Alzheimer disease (AD) is the most common form of dementia. To find a way of cure it, gene study is necessary. And gene order is a new conception of gene study currently, where gene order refers to a permutation of genes in which similar genes are ordered together one by one, and optimal gene order can be abstracted as shortest TSP route. Currently only two types of tools are reported to calculate...
A search method using an evolutionary algorithm such as a genetic algorithm (GA) is very effective if the parameter is appropriately set. However, the optimum parameter setting was so difficult that each optimal method depending on each problem pattern must be developed one by one. Therefore, this has required special expertise and large amounts of verification experiment. In order to solve this problem,...
This paper describes a hybrid meta-heuristic for combinatorial optimization problems with specific reference to the travelling salesman problem (TSP). The method is a combination of genetic algorithms (GA) and greedy randomized adaptive search procedures (GRASP). A new adaptive fuzzy greedy search operator is developed for this hybrid method. Computational experiments using a wide range of standard...
A delivery route optimization system greatly improves the real time delivery efficiency. To realize such an optimization, its distribution network requires solving several tens to hundreds (max. 1500-2000) cities Traveling Salesman Problems (TSP) within interactive response time (around 3 seconds) with expert-level accuracy (below 3% level of error rate). To meet these requirements, a Backtrack and...
Several metaheuristic algorithms based on nature-inspired phenomena have been developed to optimize non linear functions. Evolutionary systems, swarming and human immune systems have helped in development of many optimizing algorithms like genetic algorithms, particle swarm optimization and CLONALG. A novel algorithm has been proposed based on the popular belief of reincarnation where human is considered...
Finding an optimum function is not only a theoretical mathematical problem but also a particular engineering problem. Many aspects in the electrical and electronics field (by example: image processing, filter matching, optimization of network parameters, resources allocation) can be solved by finding a target function and his minimum or maximum. For such problems, usually an analytical solution is...
This paper describes a genetic algorithm for solving the traveling salesman problem (TSP) for autonomous navigation. The method is applied to autonomous underwater vehicles for efficient path planning during underwater mine inspections, sponsored by the Office of Naval Research. This method is significantly easier to implement and much more extensible to real world variants of TSP, e.g. problems incorporating...
The Game Theory-based Multi-Agent System (GTMAS) of Salhi and Töreyen, and implements a loosely coupled hybrid algorithm that may involve any number of algorithms suitable, a priori, for the solution of a given optimisation problem. The system allows the available algorithms to cooperate toward the solution of the problem in hand as well as compete for the computing facilities they require to run...
This paper has the purpose to present a new hybrid nature inspired metaheuristic developed based on three fundamentals pillars extremely well known: Genetic Algorithms, Game Theory and Fuzzy Systems. This new approach tries to mimic a little bit more closer how a population of individuals evolves along time, like human social evolution emphasizing the social interaction between individuals and the...
The generalized traveling salesman problem (GTSP) is a generalization of the classical traveling salesman problem. The GTSP is known to be an NP-hard problem and has many interesting applications. In this paper we present a local-global approach for the generalized traveling salesman problem and as well an efficient algorithm for solving the problem based on genetic algorithms. Computational results...
Memetic algorithms are highly efficient procedures to solve complex optimization problems. They combine strengths of well known metaheuristics, like the genetic algorithm (GA), with local search (LS) procedures to intensify the search. This paper proposes a computing architecture to support the execution of a memetic algorithm (MA). The Travelling Salesman Problem is elected as a case study for this...
Genetic algorithm (GA) is one of the most widely used metaheuristics in finding the approximate solutions of complex problems in a variety of domains. As such, many researchers have focused their attention on enhancing the performance of GA-in terms of either the speed or the quality but rarely in terms of both. This paper presents an efficient hybrid metaheuristic to resolve these two seemingly conflicting...
This paper considers the problem of automatically coordinating multiple platforms to explore an unknown environment. The goal is a planning algorithm that provides a path for each platform in such a way that the collection of platforms cooperatively sense the environment in a globally efficient manner. A collection of discrete locales of interest is assumed to be known and the platforms use these...
In this paper we study how the connectivity affects the performance of insular parallel genetic algorithms (PGAs). Seven topologies PGAs were proposed, with growing number of connections. We used three instances of the well-known traveling salesman problem as benchmark. Each island of the PGA had different parameters and we established a fixed migration policy for all islands. Experiments were done...
The travelling salesman problem (TSP) is one of the extensively studied optimization problem. The numerous direct applications of the TSP bring life to the research area and help to direct future work. To solve this problem many techniques have been developed. Genetic algorithm is one among those which solves this problem by using the processes observed in natural evolution to solve various optimizations...
This paper proposes a new solution for Traveling Salesman Problem (TSP), using genetic algorithm. A heuristic crossover and mutation operation have been proposed to prevent premature convergence. Presented operations try not only to solve this challenge by means of a heuristic function but also considerably accelerate the speed of convergence by reducing excessively the number of generations. By considering...
TSP is a well-known NP-hard problem. Although many algorithms for solving TSP, such as linear programming, dynamic programming, genetic algorithm, anneal algorithm, and ACO algorithm have been proven to be effective, they are not so suitable for the more complicated large scale TSP. This paper offers a method to decompose the large-scale data into several small-scale data sets by its relativity; and...
Building a delivery route optimization system that improves the delivery efficiency in real time requires to solve several tens to hundreds cities traveling salesman problems (TSP) within interactive response time, with expert-level accuracy (less than 3% of errors). To meet these requirements, a multi-inner-world genetic algorithm (Miw-GA) method is developed. This method combines several types of...
Enlightened by the synapse intensifying and adjusting mechanisms in biological neural network (BNN), this paper presents a multi-patterns elastic adjusting method, which dynamically modulates the encoded gene patterns in chromosomes, guides genetic algorithm (GA) to coordinate exploration and exploitation, therefore tries to probe and evaluate the optimizing trends of static problem from its own dynamic...
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