Multi-species evolutionary algorithms for complex optimisation problems

Evolutionary algorithms (EAs) face challenges when meeting optimisation problems that are large-scale, multi-disciplinary, or dynamic, etc. To address the challenges, this thesis focuses on developing specific and efficient multi-species EAs to deal with concurrent engineering (CE) problems and dynamic constrained optimisation problems (DCOPs). The main contributions of this thesis are: First, to achieve a better collaboration among different sub-problem optimisation, it proposes two novel collaboration strategies when using cooperative co-evolution to solve two typical kinds of CE problems. Both help to obtain designs of higher quality. An effective method is also given to adjust the communication frequency among different sub-problem optimisation. Second, it develops a novel dynamic handling strategy for DCOPs, which applies speciation methods to maintain individuals in different feasible regions. Experimental studies show that it generally reacts faster than the state-of-the-art algorithms on a test set of DCOPs. Third, it proposes another novel dynamic handling strategy based on competitive co-evolution (ComC) to address fast-changing DCOPs. It employs ComC to find a promising solution set beforehand and uses it for initialisation when detecting a change. It is shown by experiments that this strategy can help adapt to environmental changes well especially for DCOPs with very fast changes

Dynamic sampling schemes for optimal noise learning under multiple nonsmooth constraints

We consider the bilevel optimisation approach proposed by De Los Reyes, Sch\"onlieb (2013) for learning the optimal parameters in a Total Variation (TV) denoising model featuring for multiple noise distributions. In applications, the use of databases (dictionaries) allows an accurate estimation of the parameters, but reflects in high computational costs due to the size of the databases and to the nonsmooth nature of the PDE constraints. To overcome this computational barrier we propose an optimisation algorithm that by sampling dynamically from the set of constraints and using a quasi-Newton method, solves the problem accurately and in an efficient way

The epidemiological consequences of optimisation of the individual host immune response

We present a simple unscaled, quantitative framework that addresses the optimum use of resources throughout a host's lifetime based on continuous exposure to parasites (rather than evolutionary, genetically explicit trade-offs). The principal assumptions are that a host's investment of resources in growth increases its survival and reproduction, and that increasing parasite burden reduces survival. The host reproductive value is maximised for a given combination of rates of parasite exposure, host resource acquisition and pathogenicity, which results in an optimum parasite burden (for the host). Generally, results indicate that the optimum resource allocation is to tolerate some parasite infection. The lower the resource acquisition, the lower the proportion of resources that should be devoted to immunity, i.e. the higher the optimum parasite burden. Increases in pathogenicity result in reduced optimum parasite burdens, whereas increases in exposure result in increasing optimum parasite burdens. Simultaneous variation in resource acquisition, pathogenicity and exposure within a community of hosts results in overdispersed parasite burdens, with the degree of heterogeneity decreasing as mean burden increases. The relationships between host condition and parasite burden are complicated, and could potentially confound data analysis. Finally, the value of this approach for explaining epidemiological patterns, immunological processes and the possibilities for further work are discussed

From Simplistic to Complex Systems in Economics

The applicability of complex systems theory in economics is evaluated and compared with standard approaches to economic theorizing based upon constrained optimization. A complex system is defined in the economic context and differentiated from complex systems in physio-chemical and biological settings. It is explained why it is necessary to approach economic analysis from a network, rather than a production and utility function perspective, when we are dealing with complex systems. It is argued that much of heterodox thought, particularly in neo-Schumpeterian and neo-Austrian evolutionary economics, can be placed within a complex systems perspective upon the economy. The challenge is to replace prevailing 'simplistic' theories, based in constrained optimization, with 'simple' theories, derived from network representations in which value is created through the establishment of new connections between elements.

'On the Application of Hierarchical Coevolutionary Genetic Algorithms: Recombination and Evaluation Partners'

This paper examines the use of a hierarchical coevolutionary genetic algorithm under different partnering strategies. Cascading clusters of sub-populations are built from the bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations potentially search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the sub-populations on solution quality are examined for two constrained optimisation problems. We examine a number of recombination partnering strategies in the construction of higher-level individuals and a number of related schemes for evaluating sub-solutions. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements