
Dienstplanerstellung in Krankenhaeusern mittels genetischer Algorithmen
This thesis investigates the use of problemspecific knowledge to enhanc...
read it

SelfAdaptation Mechanism to Control the Diversity of the Population in Genetic Algorithm
One of the problems in applying Genetic Algorithm is that there is some ...
read it

Exploring Task Mappings on Heterogeneous MPSoCs using a BiasElitist Genetic Algorithm
Exploration of task mappings plays a crucial role in achieving high perf...
read it

cMLSGA: A CoEvolutionary MultiLevel Selection Genetic Algorithm for MultiObjective Optimization
In practical optimisation the dominant characteristics of the problem ar...
read it

Qualities, challenges and future of genetic algorithms: a literature review
Genetic algorithms, computer programs that simulate natural evolution, a...
read it

Nurse Rostering with Genetic Algorithms
In recent years genetic algorithms have emerged as a useful tool for the...
read it

Comparison of Update and Genetic Training Algorithms in a Memristor Crossbar Perceptron
Memristorbased computer architectures are becoming more attractive as a...
read it
Genetic Algorithms for MultipleChoice Problems
This thesis investigates the use of problemspecific knowledge to enhance a genetic algorithm approach to multiplechoice 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 multiplechoice 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 wellknown 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, cooperative coevolution with hierarchical subpopulations, problem structure exploiting repair schemes and indirect genetic algorithms with selfadjusting 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, problemspecific 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.
READ FULL TEXT
Comments
There are no comments yet.