In this paper a Memetic Algorithm (MA) is proposed for solving the Vehicles Routing Problem with Time Windows (VRPTW) multi-objective, using a constraint satisfaction heuristic that allows pruning of the search space to direct a search towards good solutions. An evolutionary heuristic is applied in order to establish the crossover and mutation between sub-routes. The results of MA demonstrate t...

This paper considers the single machine scheduling problem with uniform parallel machines in which the objective is to minimize the makespan. Four different GA based heuristics are designed by taking different combinations of crossover methods, viz. single point crossover method and two point crossover method, and job allocation methods while generating initial population, viz. equal number of ...

Inspired by evolutionary game theory, this paper modifies previous mixed strategy framework, adding a new mutation operator and extending to crossover operation, and proposes co-evolutionary algorithms based on mixed crossover and/or mutation strategy. The mixed mutation strategy set consists of Gaussian, Cauchy, Levy, single point and differential mutation operators; the mixed crossover strate...

In this paper, we present a novel triangulation heuristic and a new genetic algorithm to solve the problem of optimal tree decomposition of Bayesian networks. The heuristic, named MinFillWeight, aims to select variables minimizing the multiplication of the weights on nodes of fill-in edges. The genetic algorithm, named IDHGA, employs a new order-reserving crossover operator and a mutation opera...

The edge-set encoding is a direct tree representation which directly represents trees as sets of edges. There are two variants of the edge-set encoding: the edge-set encoding without heuristics, and the edge-set encoding with heuristics. An investigation into the bias of the edge-set encoding shows that the crossover operator of the edge-set encoding without heuristics is unbiased, that means i...

Genetic algorithms are random heuristic search algorithms which mimic biological evolution and molecular genetics in simplified form. These algorithms can be theoretically described by an infinite population model with the help of a deterministic dynamical system by which the stochastic population trajectory is characterized using a deterministic heuristic function and its fixed points. For pra...

Distribution of the goods from a producer to a customer is one of the most important tasks of transportation. This paper focuses on the usage of genetic algorithms (GA) for optimizing problems in transportation, namely vehicle routing problem (VRP). VRP falls in the field of NP-hard problems, which cannot be solved in polynomial time. The problem was solved using genetic algorithm with two type...

-In this paper a Meta-heuristic approach for solving the N-Queens Problem is introduced to find the best possible solution in a reasonable amount of time. Genetic Algorithm is used with a novel fitness function as the Meta-heuristic. The aim of N-Queens Problem is to place N queens on an N x N chessboard, in a way so that no queen is in conflict with the others. Chromosome representation and ge...

This paper considers inventory models with advance demand information and flexible delivery. Customers place their orders in advance, and delivery is flexible in the sense that early shipment is allowed. Specifically, an order placed at time t by a customer with demand leadtime T should be fulfilled by period t+T ; failure to fulfill it within the time window [t, t+T ] is penalized. We consider...