This book constitutes the refereed proceedings of the First Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2015, held in Newcastle, NSW, Australia, in February 2015. The 34 revised full papers presented were carefully reviewed and selected from 63 submissions. The papers are organized in the following topical sections: philosophy and theory; game environments and methods; learning, memory and optimization; and applications and implementations.

Throughout the evolutionary history of this planet, biological systems have been able to adapt, survive and ?ourish despite the turmoils and upheavals of the environment. This ability has long fascinated and inspired people to emulate and adapt natural processes for application in the arti?cial world of human endeavours. The realm of optimisation problems is no exception. In fact, in recent years biological systems have been the inspiration of the majority of meta-heuristic search algorithms including, but not limited to, genetic algorithms, particle swarmoptimisation, ant colony optimisation and extremal optimisation. This book presentsa continuum ofbiologicallyinspired optimisation, from the theoretical to the practical. We begin with an overview of the ?eld of biologically-inspired optimisation, progress to presentation of theoretical analysesandrecentextensionstoavarietyofmeta-heuristicsand?nallyshow application to a number of real-worldproblems. As such, it is anticipated the book will provide a useful resource for reseachers and practitioners involved in any aspect of optimisation problems. The overviewof the ?eld is provided by two works co-authored by seminal thinkers in the ?eld. Deb's "Evolution's Niche in Multi-Criterion Problem Solving," presents a very comprehensive and complete overview of almost all major issues in Evolutionary Multi-objective Optimisation (EMO). This chapter starts with the original motivation for developing EMO algorithms and provides an account of some successful problem domains on which EMO has demonstrated a clear edge over their classical counterparts.

In recent years, there have been many studies in which tailored heuristics and meta-heuristics have been applied to specific optimisation problems. These algorithms can be extremely efficient, but may also lack generality. In contrast, the research outlined in this monograph focuses on building a general-purpose combinatorial optimisation problem solver using a variety of meta-heuristic algorithms including Simulated Annealing and Tabu Search. The work is novel because it uses a modelling environment in which the solution is stored in dense dynamic list structures, unlike a more conventional sparse vector notation. Because of this, it incorporates a number of neighbourhood search operators that are normally only found in tailored algorithms and it performs well on a range of problems. The general nature of the outlined system allows a model developer to rapidly prototype different problems. The results indicate that the system achieves good performance in terms of solution quality and runtime on a range of combinatorial problems.

This book constitutes the refereed proceedings of the 4th Australian Conference on Artificial Life, ACAL 2009, held in Melbourne, Australia, in December 2009.

The 27 revised full papers presented were carefully reviewed and selected from 60 submissions. Research in Alife covers the main areas of biological behaviour as a metaphor for computational models, computational models that reproduce/duplicate a biological behaviour, and computational models to solve biological problems. Thus, Alife features analyses and understanding of life and nature and helps modeling biological systems or solving biological problems. The papers are organized in topical sections on alife art, game theory, evolution, complex systems, biological systems, social modelling, swarm intelligence, and heuristics.

This book constitutes the refereed proceedings of the Third Australian Conference on Artificial Life, ACAL 2007, held in Gold Coast, Australia, in December 2007.

The 34 revised full papers presented were carefully reviewed and selected from 70 submissions. Research in Alife covers the main areas of biological behaviour as a metaphor for computational models, computational models that reproduce/duplicate a biological behaviour, and computational models to solve biological problems. Thus, Alife features analyses and understanding of life and nature and helps modeling biological systems or solving biological problems. The papers are organized in topical sections on heuristics, complex systems, evolution, biological systems, and networks.

Throughout the evolutionary history of this planet, biological systems have been able to adapt, survive and ?ourish despite the turmoils and upheavals of the environment. This ability has long fascinated and inspired people to emulate and adapt natural processes for application in the arti?cial world of human endeavours. The realm of optimisation problems is no exception. In fact, in recent years biological systems have been the inspiration of the majority of meta-heuristic search algorithms including, but not limited to, genetic algorithms, particle swarmoptimisation, ant colony optimisation and extremal optimisation. This book presentsa continuum ofbiologicallyinspired optimisation, from the theoretical to the practical. We begin with an overview of the ?eld of biologically-inspired optimisation, progress to presentation of theoretical analysesandrecentextensionstoavarietyofmeta-heuristicsand?nallyshow application to a number of real-worldproblems. As such, it is anticipated the book will provide a useful resource for reseachers and practitioners involved in any aspect of optimisation problems. The overviewof the ?eld is provided by two works co-authored by seminal thinkers in the ?eld. Deb's "Evolution's Niche in Multi-Criterion Problem Solving," presents a very comprehensive and complete overview of almost all major issues in Evolutionary Multi-objective Optimisation (EMO). This chapter starts with the original motivation for developing EMO algorithms and provides an account of some successful problem domains on which EMO has demonstrated a clear edge over their classical counterparts.