This work introduces a generalised hybridisation strategy which utilises the information sharing mechanism deployed in Stochastic Diffusion Search when applied to a number of population-based algorithms, effectively merging this nature-inspired algorithm with some population-based algorithms. The results reported herein demonstrate that the hybrid algorithm, exploiting information-sharing within the population, improves the optimisation capability of some well-known optimising algorithms, including Particle Swarm Optimisation, Differential Evolution algorithm and Genetic Algorithm. This hybridisation strategy adds the information exchange mechanism of Stochastic Diffusion Search to any population-based algorithm without having to change the implementation of the algorithm used, making the integration process easy to adopt and evaluate. Additionally, in this work, Stochastic Diffusion Search has also been deployed as a global optimisation algorithm, and the optimisation capability of two newly introduced minimised variants of Particle Swarm algorithms is investigated.

This thesis investigates two major classes of Evolutionary Algorithms, Genetic Algorithms (GAs) and Evolution Strategies (ESs), and their application to the Orthogonal Packing Problems (OPP). OPP are canonical models for NP-hard problems, the class of problems widely conceived to be unsolvable on a polynomial deterministic Turing machine, although they underlie many optimisation problems in the real world. With the increasing power of modern computers, GAs and ESs have been developed in the past decades to provide high quality solutions for a wide range of optimisation and learning problems. These algorithms are inspired by Darwinian nature selection mechanism that iteratively select better solutions in populations derived from recombining and mutating existing solutions. The algorithms have gained huge success in many areas, however, being stochastic processes, the algorithms' behaviour on different problems is still far from being fully understood. The work of this thesis provides insights to better understand both the algorithms and the problems. The thesis begins with an investigation of hyper-heuristics as a more general search paradigm based on standard EAs. Hyper-heuristics are shown to be able to overcome the difficulty of many standard approaches which only search in partial solution space. The thesis also looks into the fundamental theory of GAs, the schemata theorem and the building block hypothesis, by developing the Grouping Genetic Algorithms (GGA) for high dimensional problems and providing supportive yet qualified empirical evidences for the hypothesis. Realising the difficulties of genetic encoding over combinatorial search domains, the thesis proposes a phenotype representation together with Evolution Strategies that operates on such representation. ESs were previously applied mainly to continuous numerical optimisation, therefore being less understood when searching in combinatorial domains. The work in this thesis develops highly competent ES algorithms for OPP and opens the door for future research in this area.