Publications

Dual Phase Evolution (DPE) is a widespread natural process in which complex systems adapt and self-organise by switching alternately between two phases: a phase of global interactions and a phase of local interactions. We show that in evolving networks of agents, DPE can give rise to a wide variety of topologies. In particular, it can lead to the spontaneous emergence of stabilising modular structure.

During the last three decades, the field of Intelligent and Evolutionary Systems (IES) has established itself as a key research area, with the ability to address common problems across diverse disciplines. Many challenges arise from the massive volumes of data provided by widespread automation, and from the increasingly complex webs of interaction that arise as local events scale up to generate global phenomena. Inspiration from biology – the core concept of IES research - underlies key advances in engineering systems that respond with adaptive intelligence and resilience in such noisy, dynamic environments.

Today, IES continues to develop, as traditional neural and genetic computation techniques are supplemented with new ideas from a diverse range of areas that include swarm computing, artificial life, complex systems and many others. Advances on key theoretical fronts are complemented by close ties to applications within specific areas, ensuring that IES retain relevance across disciplines.

The Asia Pacific Symposium on Intelligent and Evolutionary Systems is an annual event that brings together researchers from the Asia-Pacific Rim and other areas to discuss emerging ideas as well as on-going research in intelligent and evolutionary computation, and to deepen and extend links and collaboration in the community.

The 2008 event is the 12th meeting in a series of IES symposia that originated in 1997. This year, the event was held in Melbourne, Australia in conjunction with the 7th International Conference on Simulated Evolution and Learning (SEAL'08). The work presented at the symposium is indicative of the diverse areas touched by IES research, with applications in architecture, horticulture, epidemiology, software development and traffic control among others. In addition to exploring several new applications of genetic algorithms, papers here develop novel approaches to data assimilation, examine the theory and application of learning systems, and explore the evolution of culture and cooperation in multi-agent simulations. Key ideas such as non-dominance and entropy evolution are also further developed.

We are very grateful to the many people who have helped to make this year's symposium a success. First of all we thank the authors for their work and participation, and for providing manuscripts on time and in a standard format. All papers were carefully reviewed by at least two independent members of the program committee to ensure quality and consistency, and the time and effort contributed by the program committee are much appreciated. We are particularly grateful to Prof. David Green whose experience from previous IES meetings was invaluable for ensuring the success of this year's event. We also thank Ms Dianne Nguyen for her assistance with the event coordination and the production of these proceedings.

In this study, we describe an evolutionary mechanism – Dual Phase Evolution (DPE) – and argue that it plays a key role in the emergence of internal structure in complex adaptive systems (CAS). Our DPE theory proposes that CAS exhibit two well-defined phases – selection and variation – and that shifts from one phase to the other are triggered by external perturbations. We discuss empirical data which demonstrates that DPE processes play a prominent role in species evolution within landscapes and argue that processes governing a wide range of self-organising phenomena are similar in nature. In support, we present a simulation model of adaptive radiation in landscapes. In the model, organisms normally exist within a connected landscape in which selection maintains them in a stable state. Intermittent disturbances (such as fires, commentary impacts) flip the system into a disconnected phase, in which populations become fragmented, freeing up areas of empty space in which selection pressure lessens and genetic variation predominates. The simulation results show that the DPE mechanism may indeed facilitate the appearance of complex diversity in a landscape ecosystem.

Recent studies suggest that macro-evolutionary patterns such as punctuated equilibrium may be generated by a process termed Dual Phase Evolution (DPE). According to the DPE hypothesis, evolution in landscapes exhibits two phases – selection and variation. Disturbances such as mass extinctions can flip the landscape from selection to variation phases. Similar processes occur in a wide range of artificial, natural and social complex systems. Here, we show that mass extinctions induce DPE in a simulation model of adaptive radiation. The model is based on a previous model of adaptive radiation which did not incorporate Dual Phase Evolution. Results confirm that mass extinctions caused by external disturbances can trigger periods of rapid species turnover and adaptive radiation (variation phases), which are followed by long periods without innovation (selection phases). Our simulations also show that the spatial configuration of disasters leading to mass extinctions strongly influences whether and to what extent such disasters are capable of inducing evolutionary variation phases.

We describe a process, Dual Phase Evolution (DPE), which is a widespread mechanism by which systems evolve. The process occurs in systems that switch between two phases: a balance phase and a variation phase. In DPE, processes drive systems to settle in a “balance” phase, but external stimuli can flip the system into a temporary “variation” phase, in which rapid change occurs on all scales. The system gradually returns to a new balance phase, often with different structure than formerly. We argue that the system occurs in many natural systems and show how it results from landscape connectivity in both evolution and long-term forest change. We describe how it underlies certain optimization algorithms, and can be implemented to improve the performance of existing optimization methods. Finally, we present simulation experiments that show how DPE may play a role in creating cliques, clusters, modules and other kinds of order within social networks.