Real-world (black-box) optimization problems often involve various types of uncertainties and noise emerging in different parts of the optimization problem. When this is not accounted for,... Show moreReal-world (black-box) optimization problems often involve various types of uncertainties and noise emerging in different parts of the optimization problem. When this is not accounted for, optimization may fail or may yield solutions that are optimal in the classical strict notion of optimality, but fail in practice. Robust optimization is the practice of optimization that actively accounts for uncertainties and/or noise. Evolutionary Algorithms form a class of optimization algorithms that use the principle of evolution to find good solutions to optimization problems. Because uncertainty and noise are indispensable parts of nature, this class of optimization algorithms seems to be a logical choice for robust optimization scenarios. This thesis provides a clear definition of the term robust optimization and a comparison and practical guidelines on how Evolution Strategies, a subclass of Evolutionary Algorithms for real-parameter optimization problems, should be adapted for such scenarios. Show less
The target of this work is to extend the canonical Evolution Strategies (ES) from traditional real-valued parameter optimization domain to mixed-integer parameter optimization domain. This is... Show moreThe target of this work is to extend the canonical Evolution Strategies (ES) from traditional real-valued parameter optimization domain to mixed-integer parameter optimization domain. This is necessary because there exist numerous practical optimization problems from industry in which the set of decision variables simultaneously involves continuous, integer and discrete variables. Furthermore, objective functions of this type of problems could be based on large-scale simulation models or the structure of the objective functions may be too complex to be modeled. From this perspective, optimization problems of this kind are classified into the black-box optimization category. For them, classic optimization techniques, which come from Mathematical Programming (MP) research field, cannot be easily applied, since they are based on the assumption that the search space can always be efficiently explored using a divide-and-conquer sche me. While our new proposed algorithm, the so-called Mixed-Integer Evolution Strategies (MIES), by contrast, is capable of yielding good solutions to these challenging black-box optimization problems by using specialized variation operators tailored for mixed-integer parameter classes. In this work not only did we study MIES intensively from a theoretical point of view, but also we develop the framework for applying MIES to the real-world optimization problem in the medical field. Show less
Evolutionary Algorithms (EAs), computational problem-solvers, encode complex problems into an artificial biological environment, define its genetic operators and simulate its propagation in time.... Show moreEvolutionary Algorithms (EAs), computational problem-solvers, encode complex problems into an artificial biological environment, define its genetic operators and simulate its propagation in time. Motivated by Darwinian Evolution, it is suggested that such simulations would yield an optimal solution for the given problem. The goal of this doctoral work is to extend specific variants of EAs, namely Derandomized Evolution Strategies, to subpopulations of trial solutions which evolve in parallel to various solutions of the problem. This idea stems from the evolutionary concept of organic speciation. Such techniques are called niching methods, and they are successfully developed, at several levels, throughout the first part of the thesis. Controlling the motion of atoms has been a dream since the early days of Quantum Mechanics; The foundation of the Quantum Control field in the 1980s has brought this dream to fruition. This field has experienced an amazing increase of interest during the past 10 years, in parallel to the technological developments of ultrafast laser pulse shaping capabilities. The second part of this work is devoted to the optimization of state-of-the-art Quantum Control applications, both theoretically (simulations) and experimentally (laboratory). The application of the newly developed niching techniques successfully attains multiple laser pulse conceptual designs. Show less
