Theoretical and Empirical Evaluation of Diversity-preserving Mechanisms in Evolutionary Algorithms: On the Rigorous Runtime Analysis of Diversity-preserving Mechanisms in Evolutionary Algorithms

Evolutionary algorithms (EAs) simulate the natural evolution of species by iteratively applying evolutionary operators such as mutation, recombination, and selection to a set of solutions for a given problem. One of the major advantages of these algorithms is that they can be easily implemented when the optimisation problem is not well understood, and the design of problem-specific algorithms cannot be performed due to lack of time, knowledge, or expertise to design problem-specific algorithms. Also, EAs can be used as a first step to get insights when the problem is just a black box to the developer/programmer. In these cases, by evaluating candidate solutions it is possible to gain knowledge on the problem at hand. EAs are well suited to dealing with multimodal problems due to their use of a population. A diverse population can explore several hills in the fitness landscape simultaneously and offer several good solutions to the user, a feature desirable for decision making, multi-objective optimisation and dynamic optimisation. However, a major difficulty when applying EAs is that the population may converge to a sub-optimal individual before the fitness landscape is explored properly. Many diversity-preserving mechanisms have been developed to reduce the risk of such premature convergence and given such a variety of mechanisms to choose from, it is often not clear which mechanism is the best choice for a particular problem. We study the (expected/average) time for such algorithms to find satisfactory solutions for multimodal and multi-objective problems and to extract guidelines for the informed design of efficient and effective EAs. The resulting runtime bounds are used to predict and to judge the performance of algorithms for arbitrary problem sizes, further used to clarify important design issues from a theoretical perspective. We combine theoretical research with empirical applications to test the theoretical recommendations for their practicality, and to engage in rapid knowledge transfer from theory to practice. With this approach, we provide a better understanding of the working principles of EAs with diversity-preserving mechanisms. We provide theoretical foundations and we explain when and why certain diversity mechanisms are effective, and when they are not. It thus contributes to the informed design of better EAs

Bio-Inspired Computing For Complex And Dynamic Constrained Problems

Bio-inspired algorithms are general-purpose optimisation methods that can find solutions with high qualities for complex problems. They are able to find these solutions with minimal knowledge of a search space. Bio-inspired algorithms (the design of which is inspired by nature) can easily adapt to changing environments. In this thesis, we contribute to the theoretical and empirical understanding of bioinspired algorithms, such as evolutionary algorithms and ant colony optimisation. We address complex problems as well as problems with dynamically changing constraints. Firstly, we review the most recent achievements in the theoretical analysis of dynamic optimisation via bio-inspired algorithms. We then continue our investigations in two major areas: static and dynamic combinatorial problems. To tackle static problems, we study the evolutionary algorithms that are enhanced by using a knowledge-based mutation approach in solving single- and multi-objective minimum spanning tree (MST) problems. Our results show that proper development of biased mutation can significantly improve the performance of evolutionary algorithms. Afterwards, we analyse the ability of single- and multi-objective algorithms to solve the packing while travelling (PWT) problem. This NP-hard problem is chosen to represent real-world multi-component problems. We outline the limitations of randomised local search in solving PWT and prove the advantage of using evolutionary algorithms. Our dynamic investigations begin with an empirical analysis of the ability of simple and advanced evolutionary algorithms to optimise the dynamic knapsack (KP) problem. We show that while optimising a population of solutions can speed up the ability of an algorithm to find optimal solutions after a dynamic change, it has the exact opposite effect in environments with high-frequency changes. Finally, we investigate the dynamic version of a more general problem known as the subset selection problem. We prove the inability of the adaptive greedy approach to maintain quality solutions in dynamic environments and illustrate the advantage of using evolutionary algorithms theoretically and practically.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

A review of population-based metaheuristics for large-scale black-box global optimization: Part B

This paper is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely decomposition methods and hybridization methods such as memetic algorithms and local search. In this part we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multi-objective optimization, constraint handling, overlapping components, the component imbalance issue, and benchmarks, and applications. The paper also includes a discussion on pitfalls and challenges of current research and identifies several potential areas of future research

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance