Evolving computer programs without subtree crossover

An evolutionary programming procedure is used for optimizing computer programs in the form of symbolic expressions. Six tree mutation operators are proposed. Recombination operators such as crossover are not included. The viability and efficiency of the method is extensively investigated on a set of well-studied problems. The evidence indicates that the technique is not only viable but is indeed capable of evolving good computer programs. The results compare well with other evolutionary methods that rely on crossover to solve the same problems     

Inductive reasoning and bounded rationality reconsidered

Complex adaptive systems have historically been studied using simplifications that mandate deterministic interactions between agents or instead treat their interactions only with regard to their statistical expectation. This has led to an anticipation, even in the case of agents employing inductive reasoning in light of limited information, that such systems may have equilibria that can be predicted a priori. This hypothesis is tested here using a simulation of a simple market economy in which each agent's behavior is based on the result of an iterative evolutionary process of variation and selection applied to competing internal models of its environment. The results indicate no tendency for convergence to stability or a long-term equilibrium and highlight fundamental differences between deterministic and stochastic models of complex adaptive systems     

Evolving an expert checkers playing program without using humanexpertise

An evolutionary algorithm has taught itself how to play the game of checkers without using features that would normally require human expertise. Using only the raw positions of pieces on the board and the piece differential, the evolutionary program optimized artificial neural networks to evaluate alternative positions in the game. Over the course of several hundred generations, the program taught itself to play at a level that is competitive with human experts (one level below human masters). This was verified by playing the best evolved neural network against 165 human players on an Internet gaming zone. The neural network's performance earned a rating that was better than 99.61% of all registered players at the Website. Control experiments between the best evolved neural network and a program that relies on material advantage indicate the superiority of the neural network both at equal levels of look ahead and CPU time. The results suggest that the principles of Darwinian evolution may he usefully applied to solving problems that have not yet been solved by human expertise     

Multi-scale mechanics of nanocomposites including interface: Experimental and numerical investigation

The addition of minor amounts of fillers that have characteristic dimensions on the nanometer length scale to polymer matrix-based materials has attracted wide attention. A high aspect ratio of nanoelements and extraordinary mechanical properties (strength and flexibility) provide the ultimate reinforcement for the next generation of extremely lightweight but highly elastic and very strong advanced composite materials. However, the resultant physical behavior imparted by such an addition, has been, to date, difficult to quantify, especially for advanced thermoset composites. In an effort to better understand the phenomenological changes across multiple length- and time-scales, we first review currently reported methods of calculating nanoelement reinforced composite mechanical properties. Secondly, we describe recent experimental data along with a multi-scale modeling methodology for the calculation of elastic constants and local/interface properties for systems with statistically homogeneous distribution of embedded nanofillers (nanofibers, nanoparticles, nanoplates, or other heterogeneities, that are either aligned or randomly oriented).     

Evolution, neural networks, games, and intelligence

Mathematical games provide a framework for studying intelligent behavior in models of real-world settings or restricted domains. The obstacle comes in choosing the appropriate representation and learning algorithm. Neural networks and evolutionary algorithms provide useful means for addressing these issues. This paper describes efforts to hybridize neural and evolutionary computation to learn appropriate strategies in zero- and nonzero-sum games, including the iterated prisoner's dilemma, tic-tac-toe, and checkers. With respect to checkers, the evolutionary algorithm was able to discover a neural network that can be used to play at a near-expert level without injecting expert knowledge about how to play the game. The implications of evolutionary learning with respect to machine intelligence are also discussed. It is argued that evolution provides the framework for explaining naturally occurring intelligent entities and can be used to design machines that are also capable of intelligent behavior     

Frequencies of circular plate with concentric ring and elastic edge support

Exact solutions for the flexural vibrations of circular plates having elastic edge conditions along with rigid concentric ring support have been presented in this paper. Values of frequency parameter for the considered circular plate are computed for different sets of values of elastic rotational and translation restraints and the radius of internal rigid ring support. The results for the first three modes of plate vibrations are computed and are presented in tabular form. The effects of rotational and linear restraints and the radius of the rigid ring support on the vibration behavior of circular plates are studied over a wide range of non-dimensional parametric values. The values of the exact frequency parameter presented in this paper for varying values of restraint parameters and the radius of the rigid ring support can better serve in design and as benchmark solutions to validate the numerical methods obtained by using other methods of solution.     

Fundamental buckling of circular plates with elastically restrained edges and resting on concentric rigid ring support

This work presents the existence of buckling mode switching with respect to the radius of concentric rigid ring support. The buckling mode may not be axisymmetric as previously assumed. In general, the plate may buckle in an axisymmetric mode but when the radius of the ring support becomes small, the plate may buckle in an asymmetric mode. The optimum radius of the concentric rigid ring support for maximum buckling load is also determined. Introducing internal rigid ring support, when placed at an optimal position increases the elastic buckling load capacity by 149.39 percent. The numerical results obtained are in good agreement with the previously published data.     

Combining mutation operators in evolutionary programming

Traditional investigations with evolutionary programming for continuous parameter optimization problems have used a single mutation operator with a parametrized probability density function (PDF), typically a Gaussian. Using a variety of mutation operators that can be combined during evolution to generate PDFs of varying shapes could hold the potential for producing better solutions with less computational effort. In view of this, a linear combination of Gaussian and Cauchy mutations is proposed. Simulations indicate that both the adaptive and nonadaptive versions of this operator are capable of producing solutions that are statistically as good as, or better, than those produced when using Gaussian or Cauchy mutations alone