This paper describes the use of a genetic search method in the design of a command augmentation system for a high-performance aircraft. A genetic algorithm is used to design H-infinity controllers for the longitudinal and lateral-directional channels. The integral of absolute value of error between actual response and that of an ideal model is used as the fitness criterion. Starting from an ini...

The complexity of the selection procedure of a genetic algorithm that requires reordering, if we restrict the class of the possible fitness functions to non–local or time–dependent fitness functions, is O (N log N) where N is the size of the population. Quantum Genetic Algorithm (QGA) exploits the power of quantum computation in order to speed up genetic procedures. In QGA the classical fitness...

Some domains, like robot soccer, are difficult for agents to learn in using direct statistical and reinforcement learning techniques. However, agents often have a goal or purpose, which gives them a natural basis for reinforcement learning in the form of a fitness function. Such a fitness function allows the task of learning to be accomplished through optimization of the fitness function in the...

The notion of Pareto-optimality is one of the major approaches to multiobjective programming. While it is desirable to find more Pareto-optimal solutions, it is also desirable to find the ones scattered uniformly over the Pareto frontier in order to provide a variety of compromise solutions to the decision maker. In this paper, we design a genetic algorithm for this purpose. We compose multiple...

The quality of manufactured products is characterized by many controllable quality factors. These factors should be optimized to reach high quality products. In this paper we try to find the controllable factors levels with minimum deviation from the target and with a least variation. To solve the problem a simple aggregation function is used to aggregate the multiple responses functions then a...

We analyze the fitness dynamics of a (1+1) mutation-only genetic algorithm (GA) operating on a family of simple time-dependent fitness functions. Resulting models of behavior are used in the prediction of GA performance on this fitness function. The accuracy of performance predictions are compared to actual GA runs, and results are discussed in relation to analyses of the stationary version of ...

A new approach to design a dynamic model of genes with multiple autonomous regulatory modules by evolutionary computations is proposed. The approach is based on Genetic Algorithms (GA), with new crossover operators especially designed for these purposes. The new operators use local homology between parental strings to preserve building blocks found by the algorithm. The approach exploits the su...

Typically a search problem is posed as an optimisation task in which a cost, or fitness, function must be maximised or minimised, subject to various parameter constraints. This paper discusses “the shape of space”, in terms of search algorithms. We point out that it is the combination of representation and traversal operators that define an algorithm's view of a given search problem, and hence ...

An important goal of the theory of genetic algorithms is to build models that predict how well genetic algorithms are expected to perform on a given fitness landscape (i.e., a given combination of representation, fitness function, and set of genetic operators). This paper describes the design of a software tool called a virtual genetic algorithm (VGA) that predicts the behavior of a genetic alg...