In High Dose Rate (HDR) brachytherapy the conventional dose optimization algorithms consider the multiple objectives in form of an aggregate function which combines individual objectives into a single utility value. As a result, the optimization problem becomes single objective, prior to optimization. Up to 300 parameters must be optimized satisfying objectives which are often competing. We use...

Evolutionary optimization algorithms work with a population of solutions, instead of a single solution. Since multi-objective optimization problems give rise to a set of Pareto-optimal solutions, evolutionary optimization algorithms are ideal for handling multi-objective optimization problems. Over many years of research and application studies have produced a number of efficient multi-objectiv...

In many real-world optimization problems sparse solution vectors are often preferred. Unfortunately, evolutionary algorithms can have problems to eliminate certain components completely especially in multi-modal or neutral search spaces. A simple extension of the realvalued representation enables evolutionary algorithms to solve these types of optimization problems more efficiently. In case of ...

Computing equilibria of multiplayer noncooperative normal form games is a difficult computational task. In games having more equilibria mathematical algorithms are not capable to detect all equilibria at a time. Evolutionary algorithms are powerful search tools for solving difficult optimization problems. It is shown how an evolutionary algorithm designed for multimodal optimization can be used...

Very often real world applications have several multiple conflicting objectives. Recently there has been a growing interest in evolutionary multiobjective optimization algorithms which combines two major disciplines: evolutionary computation and the theoretical frameworks of multicriteria decision making. In this introductory chapter, we define some fundemental concepts of multiobjective optimi...

2003 2 3 Summary This thesis examines evolutionary algorithms, a universal optimization method, applied to dynamic problems, i.e. the problems are changing during optimization. The thesis is motivated by a lack of foundations for the field and the incomparability of most publications that are of an empirical nature. To establish a basis for the comparison of evolutionary algorithms applied to d...

optimizing the database queries is one of hard research problems. exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. the use of evolutionary methods, beca...

This paper presents a variation of the Euclidean Traveling Salesman Problem (TSP), the Multiple Traveling Salesman Problem (MTSP), and compares a variety of evolutionary computation algorithms and paradigms for solving it. Techniques implemented, analyzed, and discussed herein with regard to MTSP include use of a neighborhood attractor schema (a variation on k-means clustering), the "shrink-wra...

Evolutionary Algorithms (EAs) are emerging as competitive and reliable techniques for several optimization tasks. Juxtapositioning their higher-level and implicit correspondence; it is provocative to query if one optimization algorithm can benefit from another by studying underlying similarities and dissimilarities. This paper establishes a clear and fundamental algorithmic linking between part...

Mutation has traditionally been regarded as an important operator in evolutionary algorithms. In particular, there have been many experimental studies which showed the effectiveness of adapting mutation rates for various static optimization problems. Given the perceived effectiveness of adaptive and self-adaptive mutation for static optimization problems, there have been speculations that adapt...