Co-evolution vs. Neural Networks; An Evaluation of UK Risky Money

The performance of a "capital certain" Divisia index constructed using the same components included in the Bank of England"s MSI plus national savings; a "risky" Divisia index constructed by adding bonds, shares and unit trusts to the list of assets included in the first index; and a capital certain simple sum index for comparison is compared. nce suggests that co-evolutionary strategies are superior to neural networks in the majority of cases. The risky money index performs at least as well as the Bank of England Divisia index when combined with interest rate information. Notably, the provision of long term interest rates improves the out-of-sample forecasting performance of the Bank of England Divisia index in all cases examinedEvolutionary Strategies, Risk Adjusted Divisia, Inflation, Neural Networks

Evolving collective behavior in an artificial ecology

Collective behavior refers to coordinated group motion, common to many animals. The dynamics of a group can be seen as a distributed model, each “animal” applying the same rule set. This study investigates the use of evolved sensory controllers to produce schooling behavior. A set of artificial creatures “live” in an artificial world with hazards and food. Each creature has a simple artificial neural network brain that controls movement in different situations. A chromosome encodes the network structure and weights, which may be combined using artificial evolution with another chromosome, if a creature should choose to mate. Prey and predators coevolve without an explicit fitness function for schooling to produce sophisticated, nondeterministic, behavior. The work highlights the role of species’ physiology in understanding behavior and the role of the environment in encouraging the development of sensory systems

On Nash Equilibrium and Evolutionarily Stable States that Are Not Characterised by the Folk Theorem

In evolutionary game theory, evolutionarily stable states are characterised by the folk theorem because exact solutions to the replicator equation are difficult to obtain. It is generally assumed that the folk theorem, which is the fundamental theory for non-cooperative games, defines all Nash equilibria in infinitely repeated games. Here, we prove that Nash equilibria that are not characterised by the folk theorem do exist. By adopting specific reactive strategies, a group of players can be better off by coordinating their actions in repeated games. We call it a type-k equilibrium when a group of k players coordinate their actions and they have no incentive to deviate from their strategies simultaneously. The existence and stability of the type-k equilibrium in general games is discussed. This study shows that the sets of Nash equilibria and evolutionarily stable states have greater cardinality than classic game theory has predicted in many repeated games

Enumerating Knight\u27s Tours using an Ant Colony Algorithm

In this paper, we show how an ant colony optimisation algorithm may be used to enumerate knight\u27s tours for variously sized chessboards. We have used the algorithm to enumerate all tours on 5×5 and 6×6 boards, and, while the number of tours on an 8×8 board is too large for a full enumeration, our experiments suggest that the algorithm is able to uniformly sample tours at a constant, fast rate for as long as is desired

The effect of memory size on the evolutionary stability of strategies in iterated prisoner's dilemma

The iterated prisoner's dilemma is an ideal model for the evolution of cooperation among payoff-maximizing individuals. It has attracted wide interest in the development of novel strategies since the success of tit-for-tat in Axelrod's iterated prisoner's dilemma competitions. Every strategy for iterated prisoner's dilemma utilizes a certain length of historical interactions with the opponent, which is regarded as the size of the memory, in making its choices. Intuitively, longer memory strategies must have an advantage over shorter memory strategies. In practice, however, most of the well known strategies are short memory strategies that utilize only the recent history of previous interactions. In this paper, the effect of the memory size of strategies on their evolutionary stability in both infinite length and indefinite length n-person iterated prisoner's dilemma is studied. Based on the concept of a counter strategy, we develop a theoretical methodology for evaluating the evolutionary stability of strategies and prove that longer memory strategies outperform shorter memory strategies statistically in the sense of evolutionary stability. We also give an example of a memory-two strategy to show how the theoretical study of evolutionary stability assists in developing novel strategies