Adaptive simulated annealing (ASA) is a global optimization algorithm based on an associated proof that the parameter space can be sampled much more efficiently than by using other previous simulated annealing algorithms. The author's ASA code has been publicly available for over two years. During this time the author has volunteered to help people via e-mail, and the feedback obtained has been used to further develop the code.

This survey covers the state of the art of techniques for solving general purpose constrained global optimization problems and continuous constraint satisfaction problems, with emphasis on complete techniques that provably nd all solutions (if there are nitely many). The core of the material is presented in sucient detail that the survey may serve as a text for teaching constrained global optimization.

Automatic program repair has been a longstanding goal in software engineering, yet debugging remains a largely manual process. We introduce a fully automated method for locating and repairing bugs in software. The approach works on off-the-shelf legacy applications and does not require formal specifications, program annotations or special coding practices. Once a program fault is discovered, an extended form of genetic programming is used to evolve program variants until one is found that both retains required functionality and also avoids the defect in question. Standard test cases are used to exercise the fault and to encode program requirements. After a successful repair has been discovered, it is minimized using structural differencing algorithms and delta debugging. We describe the proposed method and report experimental results demonstrating that it can successfully repair ten different C programs totaling 63,000 lines in under 200 seconds, on average. 1

A model of biological evolution is considered based on Lotke-Volterra population models (ecological interaction) with mutation being modeled by introducing new species with random ecological connections. It is found that the system evolves away from the ecological equilibrium point to a sort of steady-state balancing extinction with speciation.

Genetic algorithms (GAs) are search methods based on principles of natural selection and genetics (Fraser, 1957; Bremermann, 1958; Holland, 1975). We start with a brief introduction to simple genetic algorithms and associated terminology. GAs encode

This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also

We show that the natural evolutionary algorithm for the all-pairs shortest path problem is significantly faster with a crossover operator than without. This is the first theoretical analysis proving the usefulness of crossover for a non-artificial problem. 1

Abstract not found

Experimental design and response surface methodology is applied to tuning the parameters of an optimization program employing genetic algorithms. Attention is directed to the combinatorially challenging DNA sequence assembly problem. Fine tuning of a 10K size test problem leads to a considerably improved solution to a 35K problem of sequence assembly that is of significant biological interest. Key Words and Phrases: genetic algorithms; design of experiments; response surface methods; DNA sequence assembly. R.J. PARSONS & M.E. JOHNSON 1. Introduction Design and analysis of experiments and response surface methods have prospered in this century through successful applications in agriculture initially and in industry in more recent decades. Great strides in the development of these methods have taken place owing to improvements in computing capability. Although the semiconductor industry upon which our computing platforms depend has particularly benefited from design of experiments and ...

Understanding complex behavior requires a multidisplinary effort from the neurosciences, psychology, behavioral biology, and computer science. This paper gives an overview of the current state of theoretical thinking in the field. The focus is on a behavior--oriented approach to cognition, i.e., not so much on the mental representations themselves, but on the behaviors that do require these representations. It is the intention of the paper to support the exchange between the different disciplines involved. Examples of different types of models and explanations are discussed, but no comprehensive review of all relevant work is attempted. In the second part, I collect a number of elements that in my view are essential to a future theory of cognitive behavior. Keywords: Cognition, Perception and Action, Brain Theory, Computational Theory, Artificial Life, Virtual Reality Mallot: Behavior--Oriented Approaches to Cognition Page 2 1 Introduction 1.1 Perception, Action, and Cognition The ...