The paper represents an algorithm for planning safe and optimal routes for transport facilities with unrestricted movement direction that travel within areas with obstacles. Paper explains the algorithm using a ship as an example of such a transport facility. This paper also provides a survey of several existing solutions for the problem. The method employs an evolutionary algorithm to plan several locally optimal routes and a parallel genetic algorithm to create the final route by optimising the abovementioned set of routes. The routes are optimized against the arrival time, assuming that the optimal route is the route with the lowermost arrival time. It is also possible to apply additional restriction to the routes.

In this paper, we present a parallel genetic algorithm (pGA) with adaptive control parameters and permutation representation for weighted tardiness scheduling with sequence-dependent setups, an NP-Hard problem. This pGA provides a linear to slightly superlinear speedup relative to its sequential counterpart. As part of our research, we explore the effects of different random number generation algorithms on the runtimes of both sequential and parallel GAs. GAs and other forms of evolutionary computation rely so heavily on random number generation that our results show that we can obtain a 20% increase in the speed of a pGA, and an over 25% increase in the speed of a sequential GA, simply by careful choice of random number generator---both the underlying generator as well as algorithms for specific number types such as Gaussian often needed for mutating real-valued genes.

For many classification problems, genetic algorithms prove to be effective without extensive domain engineering. However, the chess King-Rook-King endgame problem appears to be an exception. We explore whether modifications to a baseline parallel genetic algorithm can improve the accuracy on this particular problem. After describing the problem domain and our implementation of a parallel genetic algorithm, we present an empirical evaluation of several approaches intended to improve overall performance. Our results confirm the challenging nature of this domain. We describe several directions that may yet deliver significant improvements.

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.