Niching in derandomized evolution strategies and its applications in quantum control

Evolutionary 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.FOM, The Dutch Foundation for Research on Fundamental ResearchUBL - phd migration 201

The unreasonable effectiveness of the final batch normalization layer

Early-stage disease indications are rarely recorded in real-world domains, such as Agriculture and Healthcare, and yet, their accurate identification is critical in that point of time. In this type of highly imbalanced classification problems, which encompass complex features, deep learning (DL) is much needed because of its strong detection capabilities. At the same time, DL is observed in practice to favor majority over minority classes and consequently suffer from inaccurate detection of the targeted early-stage indications. In this work, we extend the study done by  [11], showing that the final BN layer, when placed before the softmax output layer, has a considerable impact in highly imbalanced image classification problems as well as undermines the role of the softmax outputs as an uncertainty measure. This current study addresses additional hypotheses and reports on the following findings: (i) the performance gain after adding the final BN layer in highly imbalanced settings could still be achieved after removing this additional BN layer in inference; (ii) there is a certain threshold for the imbalance ratio upon which the progress gained by the final BN layer reaches its peak; (iii) the batch size also plays a role and affects the outcome of the final BN application; (iv) the impact of the BN application is also reproducible on other datasets and when utilizing much simpler neural architectures; (v) the reported BN effect occurs only per a single majority class and multiple minority classes – i.e., no improvements are evident when there are two majority classes; and finally, (vi) utilizing this BN layer with sigmoid activation has almost no impact when dealing with a strongly imbalanced image classification tasks.Algorithms and the Foundations of Software technolog

Benchmarking and analyzing iterative optimization heuristics with IOHprofiler

Algorithms and the Foundations of Software technolog

Addressing the multiplicity of solutions in optical lens design as a niching evolutionary algorithms computational challenge

Algorithms and the Foundations of Software technolog

Niche radius adaptation in the CMA-ES niching algorithm

Niching methods are the extension of Evolutionary Algorithms (EAs) to multi-modal optimization: they allow parallel convergence into multiple good solutions by maintaining the diversity of certain properties within the population. The majority of the EAs Niching methods holds an assumption concerning the fitness landscape, stating that the peaks are far enough from one another with respect to some threshold distance, called the niche radius, which is estimated for the given problem and remains fixed during the course of evolution. Obviously, there are landscapes for which this assumption isn’t applicable, and where those niching methods are most likely to fail. This is the so-called niche radius problem

Benchmarking discrete optimization heuristics with IOHprofiler

Algorithms and the Foundations of Software technolog

Evolutionary optimization of rotational population transfer

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