This article reviews the growing body of scientific work in artificial chemistry. First, common motivations and fundamental concepts are introduced. Second, current research activities are discusse...

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Artificial chemistries—a review

Research into adjusting the probabilities of crossover and mutation pm in genetic algorithms (GAs) is one of the most significant and promising areas in evolutionary computation. px and pm greatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution efficiently. Instead of using fixed values of px and pm , this paper presents the use of fuzzy logic to adaptively adjust the values of px and pm in GA. By applying the K-means algorithm, distribution of the population in the search space is clustered in each generation. A fuzzy system is used to adjust the values of px and pm. It is based on considering the relative size of the cluster containing the best chromosome and the one containing the worst chromosome. The proposed method has been applied to optimize a buck regulator that requires satisfying several static and dynamic operational requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA using fixed values of px and pm. The effectiveness of the fuzzy-controlled crossover and mutation probabilities is also demonstrated by optimizing eight multidimensional mathematical functions

Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms

We have developed a genetic algorithm (GA) approach to the problem of task scheduling for multiprocessor systems. Our approach requires minimal problem specific information and no problem specific operators or repair mechanisms. Key features of our system include a flexible, adaptive problem representation and an incremental fitness function. Comparison with traditional scheduling methods indicates that the GA is competitive in terms of solution quality if it has sufficient resources to perform its search. Studies in a nonstationary environment show the GA is able to automatically adapt to changing targets.

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An incremental genetic algorithm approach to multiprocessor scheduling

Detecting autocatalytic, self-sustaining sets in chemical reaction systems

Thomas L. Sterling, John Salmon, Donald J. Becker, Savarese, Daniel F. Savarese MIT Press, Cambridge, MA, 1999, 250 pp. ISBN: 026269218X, $31.95 The authors of this book have attempted to describe in 232 pages a subject which is difficult to fit into a book and which is rapidly evolving. Their stated purpose is "enabling, facilitating, and accelerating the adoption of the Beowulf model of distributed computing." They provide an in-depth view of one possible Beowulf system covering details of hardware selection, operating system configuration, communication software and a parallel sorting application. Overall the book is informative enough to convince people of the value of using Beowulf clustering and basically fulfills the stated purpose, but the subject matter is too broad for a single book. The book begins with an overview of Beowulf systems including some background material on parallel computers and outlining the rest of the book. This discussion on parallel computers in general is quite brief. The authors point out the importance of the recent increases in performance of mass-market computers, which is the reason why Beowulf clusters exist. They continue this section by giving an overview of the hardware and software components of the Beowulf cluster. Following the introduction is a discussion of the hardware elements used to build a typical Beowulf cluster. This discussion is nicely written and introduces topics such as the PCI bus, types of memory, and motherboards. The details are useful now, although the details of hardware design evolve too rapidly for this discussion to be as useful several years from now. Next, the authors introduce the Linux operating system. This discussion is not detailed enough to guide a user through installation and configuration, but it does provide an overview and it includes a useful list of references both printed and electronic. Following the Linux discussion, the authors sketch networking related issues. They discuss most available network hardware solutions and discuss Ethernet in sufficient detail to understand its performance. Their discussion of TCP/IP is detailed enough to understand IP addressing but does not include a discussion of routing. Overall, this discussion is a mixture of overviews of topics and detailed discussions. The authors provide a useful discussion of how to manage a Beowulf cluster. They suggest practical methods of cloning nodes using Linux tools and they describe methods for day-to-day administration. This discussion is possibly the most useful contribution of the book. Most of the book consists of topics covered on more detail in on-line resources, but managing a Beowulf cluster is a little off the beaten path. Next, the authors discuss parallelism. This includes a categorization of parallel algorithms along with an introduction to a variety of parallel performance metrics. They also give a useful introduction to MPI including a nice exposition about a sorting example. Their example provides a good example of MPI programming along with a useful analysis of the performance characteristics of the application. Overall, the authors present a useful discussion of Beowulf clusters. Most topics are discussed lightly while a few are discussed in-depth. The reader should not expect this book to answer every question encountered in configuring and using a Beowulf cluster. Instead this book offers an overview of the process and details can be obtained by consulting manual pages, Linux Howto documents and on-line resources about Linux and MPI. Benjamin R. Seyfarth  University of Southern Mississippi

How to Build a Beowulf: A Guide to the Implementation and Application of PC Clusters

High-level analog circuit design is a complex problem domain in which evolutionary search has recently produced encouraging results. However, little is known about how to best structure evolution far these tasks. The choices of circuit representation, fitness evaluation technique, and genetic operators clearly have a profound effect on the search process. In this paper, we examine fitness evaluation by comparing the effectiveness of four fitness schedules. Three fitness schedules are dynamic-the evaluation function changes over the course of the run, and one is static. Coevolutionary search is included, and we present a method of evaluating the problem population that is conducive to multiobjective optimization. Twenty-five runs of an analog amplifier design task using each fitness schedule are presented. The results indicate that solution quality is highest with static and coevolving fitness schedules as compared to the other two dynamic schedules. We discuss these results and offer two possible explanations for the observed behavior: retention of useful information, and alignment of problem difficulty with circuit proficiency.

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A comparison of dynamic fitness schedules for evolutionary design of amplifiers

Parallelized versions of genetic algorithms (GAs) are popular primarily for three reasons: the GA is an inherently parallel algorithm, typical GA applications are very compute intensive, and powerful computing platforms, especially Beowulf-style computing clusters, are becoming more affordable and easier to implement. In addition, the low communication bandwidth required allows the use of inexpensive networking hardware such as standard office ethernet. In this paper we describe a parallel GA and its use in automated high-level circuit design. Genetic algorithms are a type of trial-and-error search technique that are guided by principles of Darwinian evolution. Just as the genetic material of two living organisms can intermix to produce offspring that are better adapted to their environment, GAs expose genetic material, frequently strings of 1s and Os, to the forces of artificial evolution: selection, mutation, recombination, etc. GAs start with a pool of randomly-generated candidate solutions which are then tested and scored with respect to their utility. Solutions are then bred by probabilistically selecting high quality parents and recombining their genetic representations to produce offspring solutions. Offspring are typically subjected to a small amount of random mutation. After a pool of offspring is produced, this process iterates until a satisfactory solution is found or an iteration limit is reached. Genetic algorithms have been applied to a wide variety of problems in many fields, including chemistry, biology, and many engineering disciplines. There are many styles of parallelism used in implementing parallel GAs. One such method is called the master-slave or processor farm approach. In this technique, slave nodes are used solely to compute fitness evaluations (the most time consuming part). The master processor collects fitness scores from the nodes and performs the genetic operators (selection, reproduction, variation, etc.). Because of dependency issues in the GA, it is possible to have idle processors. However, as long as the load at each processing node is similar, the processors are kept busy nearly all of the time. In applying GAs to circuit design, a suitable genetic representation 'is that of a circuit-construction program. We discuss one such circuit-construction programming language and show how evolution can generate useful analog circuit designs. This language has the desirable property that virtually all sets of combinations of primitives result in valid circuit graphs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. Using a parallel genetic algorithm and circuit simulation software, we present experimental results as applied to three analog filter and two amplifier design tasks. For example, a figure shows an 85 dB amplifier design evolved by our system, and another figure shows the performance of that circuit (gain and frequency response). In all tasks, our system is able to generate circuits that achieve the target specifications.

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A Parallel Genetic Algorithm for Automated Electronic Circuit Design

The relatively new field of Evolvable Hardware studies how simulated evolution can reconfigure, adapt, and design hardware structures in an automated manner. Space applications, especially those requiring autonomy, are potential beneficiaries of evolvable hardware. For example, robotic drilling from a mobile platform requires high-bandwidth controller circuits that are difficult to design. In this paper, we present automated design techniques based on evolutionary search that could potentially be used in such applications. First, we present a method of automatically generating analog circuit designs using evolutionary search and a circuit-construction language. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. Using a parallel genetic algorithm, we present experimental results for five design tasks. Second, we investigate the use of coevolution in automated circuit design. We examine fitness evaluation by comparing the effectiveness of four fitness schedules. The results indicate that solution quality is highest with static and coevolving fitness schedules as compared to the other two dynamic schedules. We discuss these results and offer two possible explanations for the observed behavior: retention of useful information, and alignment of problem difficulty with circuit proficiency.

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Towards evolving electronic circuits for autonomous space applications

We construct simple artificial chemistries in order to gain an understanding of how a chemical reaction network might emerge from a state of relative disorder in non-living "protocells". Such chemistries have relevance to origin-of-life studies as well as to artificial life research. We present a model comprised of interacting polymers, and specify two initial conditions: a distribution of relatively disordered polymers and a fixed set of reversible catalytic reactions. A genetic algorithm is then used to find a set of reactions that exhibit a pre-specified behavior. Our results show that reaction sets can be found to give polymer distributions that are biased towards longer polymers. We present examples of these protocell chemistries and show that the reaction sets found are robust in the sense that they produce desirable behavior in equilibrium. Such a technique is useful because it allows an investigator to determine whether a specific distribution can be produced, and if it can, a reaction network can be found and then analyzed.

Evolving catalytic reaction sets using genetic algorithms

This paper explores a coevolutionary approach applicable to difficult problems with limited failure/success performance feedback. Like familiar "predator-prey" frameworks this algorithm evolves two populations of individuals - the solutions (predators) and the problems (prey). The approach extends previous work by rewarding only the problems that match their difficulty to the level of solution competence. In complex problem domains with limited feedback, this "tractability constraint" helps provide an adaptive fitness gradient that effectively differentiates the candidate solutions. The algorithm generates selective pressure toward the evolution of increasingly competent solutions by rewarding solution generality and uniqueness and problem tractability and difficulty. Relative (inverse-fitness) and absolute (static objective function) approaches to evaluating problem difficulty are explored and discussed. On a simple control task, this coevolutionary algorithm was found to have significant advantages over a genetic algorithm with either a static fitness function or a fitness function that changes on a hand-tuned schedule.

Gary L. Haith

Papers

A comparison of dynamic fitness schedules for evolutionary design of amplifiers

A Parallel Genetic Algorithm for Automated Electronic Circuit Design

Towards evolving electronic circuits for autonomous space applications

Evolving catalytic reaction sets using genetic algorithms

Coevolution for problem simplification