Genetic Algorithms Optimizing genetic algorithms for time critical problems Abstract Genetic algorithms have a lot of attributes that makes it a good choice when one needs to solve very complicated problems. The performance of genetic algorithms is aected by the parameters that are used. Optimization of the parameters for the genetic algorithm is one of the most popular research fields of genetic algorithms. One of the reasons for this is because of the complicated relation between the parameters and factors such as the complexity of the problem. This thesis describes what happens when time is added to this problem. One of the most important parameters is population size and we have found by testing a wide range of problems that the optimal population size is not the same as if time was not involved. Keywords: Genetic Algorithms, Time Critical, Optimization, Population Size File: The report (pdf)		Java and AspectJ Lists and Huffman coding Example of Linked lists and Huffman coding. Aspect Oriented Programming Introduction to Aspect Oriented Programming. Aspect Tetris The game Tetris made in AspectJ.   Artificial Intelligence Genetic Algorithms Optimizing Genetic Algorithms for time critical problems.   Requirement and V&V Requirement engenering How to handle requirements when developing market driven products? Inspection vs Testing Which technique is best? Verification & Validation Introduction to the field of verification and validation for the newcomers.   Computer Science RAID An overview of how RAID works. Human Computer Interaction What good is the technology if it too complex to use?
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