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In this paper a thermodynamic approach is presented to the problem of convergence of evolutionary algorithms. The case of the Simulated Annealing algorithm for optimisation is considered as a simple evolution strategy wi...

The use of evolutionary and swarm intelligence algorithms, has become a very popular option to solve complex real-world optimization problems. However, in their original versions, these algorithms lack a mechanism to han...

Evolutionary Computation (EC) have been applied to a great number of optimisation problems with some success, specially in fields such as production operations in manufacturing industry, parallel and distributed systems,...

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most ...

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most ...

Evolutionary Computation is an emergent field, which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. Exploration and exploitation of solution i...

Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manip...

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most ...

"Various tools for program analysis, including run-time assertion checkers and static analyzers such as verification and test generation tools, require formal specifications of the programs being analyzed. Moreover, many...

This paper presents an encoding technique that is common for many different logic synthesis problems. It enables us to construct a system of Boolean functions, and then to decompose this system into sub-systems in such a...

Determining an optimal schedule to minimize the completion time of the last job abandoning the system (makespan) becomes a very difficult problem when there are more than two machines in the flow shop. Due both to its ec...

This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to ...

Over the past few years, a continually increasing number of research efforts have investigated the application of evolutionary computation techniques for the solution of scheduling problems. Scheduling problems can pose...

Allocation of the components (tasks) of a parallel program to processors in a multiprocessor or a multicomputer system take full advantage of the computational power provided by the system. Evolutionary approaches has b...

Over the past three decades extensive search have been done on pure m-machine flow shop problems. Many researchers faced the Flow Shop Scheduling Problem (FSSP) by means of well-known heuristics which, are successfully ...

Scheduling is an active area of research in applied artificial intelligence. Scheduling problems typically comprise several concurrent (and often conflicting) goals, and several resources which may be allocated in order ...

This work summarizes results when facing the problem of allocating a number of nonidentical tasks in a parallel system. The model assumes that the system consists of a number of identical processors and that only one tas...

Determining an optimal schedule to m1mm1ze the completion time of the last job abandoning the system (makespan) become a very difficult problem when there are more than two machines in the flow shop. Due both to its econ...

The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark test...

This work studies the solution space topology of the Traveling Salesman Problem or TSP, as a bi-objective optimization problem. The concepts of category and range of a solution are introduced for the first time in this ...

We present a resolution technique of the University course Timetabling problem (UCTP), this technique is based in the implementation of Hypercube framework using the Max-Min Ant System. We presented the structure of the ...

This paper presents a new traffic engineering multitreemultiobjective multicast routing algorithm (M-MMA) that solves for the first time the GMM model for Dynamic Multicast Groups. Multitree traffic engineering uses seve...

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describe...

Fire modelling is used by engineers and scientists to understand and to predict possible fire behaviour. Empirical, semi-empirical, and physical models have been developed to predict wildfire behaviour. Any of these can ...