This paper examines the potential of a neural network coupled with genetic algorithms to recognize the parameters that define the production curve of sheep milk, in which production is time-dependent, using solely the data registered in the animals’ first controls. This enables the productive capacity of the animal to be identified more rapidly and leads to a faster selection process in determi...

This paper describes a Genetic Algorithms approach to a manpower-scheduling problem arising at a major UK hospital. Although Genetic Algorithms have been successfully used for similar problems in the past, they always had to overcome the limitations of the classical Genetic Algorithms paradigm in handling the conflict between objectives and constraints. The approach taken here is to use an indi...

An iterative algorithm is proposed and tested in Cournot Game models, which is based on the convergence of sequential best responses and the utilization of a genetic algorithm for determining each player’s best response to a given strategy profile of its opponents. An extra outer loop is used, to address the problem of finite accuracy, which is inherent in genetic algorithms, since the set of f...

We investigate sublinear classical and quantum algorithms for matrix games, a fundamental problem in optimization machine learning, with provable guarantees. Given matrix, the game were previously known only two special cases: (1) maximizing vectors live L1-norm unit ball, (2) minimizing either L1- or L2-norm ball. give algorithm that can interpolate smoothly between these any fixed q 1 2, we s...

Genetic Algorithms have gained wide popularity due to their conceptual simplicity and their ability to obtain approximate solutions for complex searching and optimization problems. Their wide applicability contrasts with their rather undeveloped theoretical basis. In particular, the convergence of genetic algorithms has been one of the most challenging theoretical issues in the evolutionary com...