evolutionary algorithms are some of the most crucial random approaches tosolve the problems, but sometimes generate low quality solutions. on the otherhand, learning automata are adaptive decision-making devices, operating onunknown random environments, so it seems that if evolutionary and learningautomaton based algorithms are operated simultaneously, the quality of results willincrease sharpl...

The capability of multi-objective evolutionary algorithms (MOEAs) to handle premature convergence is critically important when applied to realworld problems. Their highly multi-modal and discrete search space often makes the required performance out of reach to current MOEAs. Examining the fundamental cause of premature convergence in evolutionary search has led to proposing of a generic framew...

There are three important functions of manufacturing which process planning, scheduling and due date assignment. Instead executing these separately, combining them together helps making more realistic applicable plans. In this problem context random search, semi-tabu, random/semi-tabu hybrid, evolutionary strategies, random/evolutionary strategies hybrid methods utilized in solution. Different ...

our ancestors without fear, worry and anxiety are not our ancestors, as organisms without defense systems were extinct. therefore, while behavior such as fear and anxiety are useful to prevent harm, the exaggerated form, that is, anxiety disorders, has more costs than benefits. likewise, while a panic attack is a normal and useful response in the face of life-threatening danger, a panic disorde...

In the GA approach the parameters that influence its performance include population size, crossover rate and mutation rate. Genetic algorithms are suitable for traversing large search spaces since they can do this relatively fast and because the mutation operator diverts the method away from local optima, which will tend to become more common as the search space increases in size. GA’s are base...

Gaphyl is an application of genetic algorithms (GA's) to phylogenetics, an approach used by biologists to investigate the evolutionary relationships among organisms. Typical phylogenetic software packages use heuristic search methods to navigate through a space of possible trees in an attempt to nd the most plausible evolutionary hypotheses, as exhaustive search is not practical in this domain....

In both numerical and stochastic optimization methods, surrogate models are often employed in lieu of the expensive high-fidelity models to enhance search efficiency. In gradient-based numerical methods, the trustworthiness of the surrogate models in predicting the fitness improvement is often addressed using ad hoc move limits or a trust region framework (TRF). Inspired by the success of TRF i...

Grammatical Evolution is an evolutionary algorithm that can evolve complete programs using a Backus Naur form grammar as a plug-in component to describe the output language. An important issue of Grammatical Evolution, and evolutionary computation in general, is the difficulty in dealing with deceptive problems and avoid premature convergence to local optima. Novelty search is a recent techniqu...

In this paper we shall give a mathematical description of a general evolutionary heuristic search algorithm which allows to see a very special property which slightly generalized binary genetic algorithms have comparing to other evolutionary computation techniques. It turns out that such a generalized genetic algorithm, which we call a binary semi-genetic algorithm, is capable of encoding virtu...

In this paper we focus on optimization of database schema transformation, by evolutionary (or genetic) search. A framework for transforming conceptual data schemata into eecient internal schemata is presented. We consider this problem from the viewpoint of searching through the space of all correct, but possibly incomplete internal representations of the conceptual schema at hand. A search stra...