A Study of NK Landscapes' Basins and Local Optima Networks

We propose a network characterization of combinatorial fitness landscapes by adapting the notion of inherent networks proposed for energy surfaces (Doye, 2002). We use the well-known family of $NK$ landscapes as an example. In our case the inherent network is the graph where the vertices are all the local maxima and edges mean basin adjacency between two maxima. We exhaustively extract such networks on representative small NK landscape instances, and show that they are 'small-worlds'. However, the maxima graphs are not random, since their clustering coefficients are much larger than those of corresponding random graphs. Furthermore, the degree distributions are close to exponential instead of Poissonian. We also describe the nature of the basins of attraction and their relationship with the local maxima network.Comment: best paper nominatio

The Self-Organization of Interaction Networks for Nature-Inspired Optimization

Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system

The Self-Organization of Interaction Networks for Nature-Inspired Optimization

Over the last decade, significant progress has been made in understanding complex biological systems, however there have been few attempts at incorporating this knowledge into nature inspired optimization algorithms. In this paper, we present a first attempt at incorporating some of the basic structural properties of complex biological systems which are believed to be necessary preconditions for system qualities such as robustness. In particular, we focus on two important conditions missing in Evolutionary Algorithm populations; a self-organized definition of locality and interaction epistasis. We demonstrate that these two features, when combined, provide algorithm behaviors not observed in the canonical Evolutionary Algorithm or in Evolutionary Algorithms with structured populations such as the Cellular Genetic Algorithm. The most noticeable change in algorithm behavior is an unprecedented capacity for sustainable coexistence of genetically distinct individuals within a single population. This capacity for sustained genetic diversity is not imposed on the population but instead emerges as a natural consequence of the dynamics of the system

An artificial life approach to evolutionary computation: from mobile cellular algorithms to artificial ecosystems

This thesis presents a new class of evolutionary algorithms called mobile cellular evolutionary algorithms (mcEAs). These algorithms are characterized by individuals moving around on a spatial population structure. As a primary objective, this thesis aims to show that by controlling the population density and mobility in mcEAs, it is possible to achieve much better control over the rate of convergence than what is already possible in existing cellular EAs. Using the observations and results from this investigation into selection pressure in mcEAs, a general architecture for developing agent-based evolutionary algorithms called Artificial Ecosystems (AES) is presented. A simple agent-based EA is developed within the scope of AES is presented with two individual-based bottom-up schemes to achieve dynamic population sizing. Experiments with a test suite of optimization problems show that both mcEAs and the agent-based EA produced results comparable to the best solutions found by cellular EAs --Abstract, page iii

Takeover time curves in random and small-world structured populations

We present discrete stochastic mathematical models for the growth curves of synchronous and asynchronous evolutionary algorithms with populations structured according to a random graph. We show that, to a good approximation, randomly structured and panmictic populations have the same growth behavior. Furthermore, we show that global selection intensity depends on the update policy. The validity of the models is confirmed by a comparison with experimental results of simulations. We also present experimental results on small-world and scale-free population graph topologies. We show that they lead to qualitatively similar results. However, the different nature of the nodes can be exploited to obtain a more varied evolutionary behavior