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GAS are naturally suited to parallel implementation, and a number of approaches to parallelization have been explored Coarse grain approaches to parallelization subdivide the population into somewhat distinct groups of individuals, called demes Each deme is assigned to a different computational node, and a standard GA search is performed at each node Communication and cross-fertilization between demes occurs on a less frequent basis than within demes Transfer between demes occurs by a migration process, in which individuals from one deme are copied or transferred to other demes This process is modeled after the kind of cross-fertilization that might occur between physically separated subpopulations of biological species One benefit of such approaches is that it reduces the crowding problem often encountered in nonparallel GAS, in which the system falls into a local optimum due to the early appearance of a genotype that comes to dominate the entire population Examples of coarse-grained parallel GAS are described by Tanese (1989) and by Cohoon et al (1987) In contrast to coarse-grained parallel implementations of GAS, fine-grained implementations typically assign one processor per individual in the population Recombination then takes place among neighboring individuals Several different types of neighborhoods have been proposed, ranging from planar grid to torus Examples of such systems are described by Spiessens and Manderick (1991) An edited collection of papers on parallel GAS is available in Stender (1993)

COMMUNITY SERVICE Member, PhD dissertation committees for T L Smith (Astronomy 1990 1992) and AO Schwartz (Astrophysics 1991 1993), University of Washington Member, Committee on Affiliation Services, Lick Observatory, University of California, Santa Cruz, CA1990 present Member, University committee on user services, Kitt Peak National Observatory and Dominion Astrophysical Observatory 1991 present

The main points of this chapter include:

By the way, there are a number of different options for running this script, giving you finer control over what you remove. Check out the documentation for this in About Xcode.pdf (which you can find in the same folder as the installation package). Note that you need to run this script with root privilege, hence the sudo command. It s worth checking that the /Developer/Library/ folder has been removed, and if not just move that to the Trash, too.

Genetic algorithms (GAS) conduct a randomized, parallel, hill-climbing search for hypotheses that optimize a predefined fitness function The search performed by GAS is based on an analogy to biological evolution A diverse population of competing hypotheses is maintained At each

iteration, the most fit members of the population are selected to produce new offspring that replace the least fit members of the population Hypotheses are often encoded by strings that are combined by crossover operations, and subjected to random mutations GAS illustrate how learning can be viewed as a special case of optimization In particular, the learning task is to find the optimal hypothesis, according to the predefined fitness function This suggests that other optimization techniques such as simulated annealing can also be applied to machine learning problems GAS have most commonly been applied to optimization problems outside machine learning, such as design optimization problems When applied to learning tasks, GAS are especially suited to tasks in which hypotheses are complex (eg, sets of rules for robot control, or computer programs), and in which the objective to be optimized may be an indirect function of the hypothesis (eg, that the set of acquired rules successfully controls a robot) Genetic programming is a variant of genetic algorithms in which the hypotheses being manipulated are computer programs rather than bit strings Operations such as crossover and mutation are generalized to apply to programs rather than bit strings Genetic programming has been demonstrated to learn programs for tasks such as simulated robot control (Koza 1992) and recognizing objects in visual scenes (Teller and Veloso 1994)

You start up Xcode just like any other Mac application: either click on the Dock icon (if it is there) or double-click on the icon in /Developer/Applications (you didn t install it there OK, wherever you did install it).

Evolution-based computational approaches have been explored since the early days of computer science (eg, Box 1957 and Bledsoe 1961) Several different evolutionary approaches were introduced during the 1960s and have been further explored since that time Evolution strategies, developed by Rechenberg (1965, 1973) to optimize numerical parameters in engineering design, were followed up by Schwefel (1975, 1977, 1995) and others Evolutionary programming, developed by Folgel, Owens, and Walsh (1966) as a method for evolving finite-state machines, was followed up by numerous researchers (eg, Fogel and Atmar 1993) Genetic algorithms, introduced by Holland (1962, 1975) included the notion of maintaining a large population of individuals and emphasized crossover as a key operation in such systems Genetic programming, introduced by Koza (1992), applies the search strategy of genetic algorithms to hypotheses consisting of computer programs As computer hardware continues to become faster and less expensive, interest in evolutionary approaches continues to grow One approach to using GAS to learn sets of rules was developed by K DeJong and his students at the University of Pittsburgh (eg, Smith 1980) In this approach, each rule set is one member in the population of competing hypotheses, as in the GABIL system discussed in this chapter A somewhat different approach was developed at University of Michigan by Holland and his students (Holland 1986), in which each rule is a member of the population, and