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Holland, J H (1986) Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems In R Michalski, J Carbonell, & T Mitchell (Eds), Machine learning: An artijicial intelligence approach (Vol 2) San Mateo, CA: Morgan Kaufmann Holland, J H (1989) Searching nonlinear functions for high values Applied Mathematics and Computation, 32, 255-274 Janikow, C Z (1993) A knowledge-intensive GA for supervised learning Machine Learning, 13, 189-228 Koza, J (1992) Genetic programming: On the programming of computers by means of natural selection Cambridge, MA: MIT Press Koza, J R (1994) Genetic Programming 11: Automatic discovery of reusable programs Cambridge, MA: The MIT Press Koza, J R, Bennett 111, F H, Andre, D, & Keane, M A (1996) Four problems for which a computer program evolved by genetic programming is competitive with human performance Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (pp 1-10) IEEE Press Koza, J R, Goldberg, D E, Fogel, D B, & Riolo, R L (Eds) (1996b) Genetic programming 19%: Proceedings of the First Annual Conference Cambridge, MA: MIT Press Machine Learning: Special Issue on Genetic Algorithms (1988) 3:2-3, October Machine Learning: Special Issue on Genetic Algorithms (1990) 5:4, October Machine karning: Special Issue on Genetic Algorithms (1993) l3:2,3, November Mitchell, M (1996) An introduction to genetic algorithms Cambridge, MA: MIT Press O'Reilly, U-M, & Oppacher, R (1994) Program search with a hierarchical variable length representation: Genetic programming, simulated annealing, and hill climbing In Y Davidor et al (Eds), Parallel problem solving from nature-PPSN I11 (Vol 866) (Lecture notes in computer science) Springer-Verlag Rechenberg, I (1965) Cybernetic solution path of an experimental problem Ministry of aviation, Royal Aircraft Establishment, UK Rechenberg, I (1973) Evolutionsstrategie: Optimierung technischer systeme nach prinzipien der biolgischen evolution Stuttgart: Frommann-Holzboog Schwefel, H P (1975) Evolutionsstrategie und numerische optimiemng (PhD thesis) Technical University of Berlin Schwefel, H P (1977) Numerische optimierung von computer-modellenmittels der evolutionsstrategie Basel: Birkhauser Schwefel, H P (1995) Evolution and optimum seeking New York: John Wiley & Sons Spiessens, P, & Manderick, B (1991) A massively parallel genetic algorithm: Implementation and first analysis Proceedings of the 4th International Conference on Genetic Algorithms (pp 279-286) Smith, S (1980) A learning system based on genetic adaptive algorithms (PhD dissertation) Computer Science, University of Pittsburgh Stender, J (Ed) (1993) Parallel genetic algorithms Amsterdam: IOS Publishing Tanese, R (1989) Distributed genetic algorithms Proceedings of the 3rd International Conference on Genetic Algorithms (pp 434-439) Teller, A, & Veloso, M (1994) PADO: A new learning architecture for object recognition In K Ikeuchi & M Veloso (Eds), Symbolic visual learning @p 81-116) Oxford, England: Oxford Univ Press Turney, P D (1995) Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm Journal of Al Research, 2, 369-409 http://wwwcswashingtonedu/ research/jair/homehtmI.

Listing 7 8. The Corrected ColorController.m Code #import "ColorController.h" @implementation ColorController @synthesize redIntValue; @synthesize greenIntValue; @synthesize blueIntValue; @synthesize alphaFloatValue; - (void)awakeFromNib { [self setValue:[NSNumber [self setValue:[NSNumber [self setValue:[NSNumber [self setValue:[NSNumber [self updateColorWell]; } - (IBAction)setNewColor:(id)sender { [self updateColorWell]; } - (void)updateColorWell { NSLog(@"Updating the Color Well with Red value: %d Green value: %d Blue value: %d Alpha value %f", redIntValue, greenIntValue, blueIntValue, alphaFloatValue); NSColor *theColor = [NSColor colorWithCalibratedRed:((float)redIntValue/255) green:((float)greenIntValue/255) blue:((float)blueIntValue/255) alpha:(alphaFloatValue)]; [colorWell setColor:theColor]; } @end numberWithInt:51] forKey:@"redIntValue"]; numberWithInt:102] forKey:@"greenIntValue"]; numberWithInt:153] forKey:@"blueIntValue"]; numberWithFloat:1.0] forKey:@"alphaFloatValue"];

Tumey, P D, Whitley, D, & Anderson, R (1997) Evolutionary Computation Special issue: The Baldwin effect, 4(3) Cambridge, MA: MIT Press http://www-mitpressmiteduljmlscatalog/evolution-abstracts/evolhtml Whitley, L D, & Vose, M D (Eds) (1995) Foundations of genetic algorithms 3 Morgan Kaufmann Wright, S (1977) Evolution and the genetics of populations Vol 4: Variability within and among Natural Populations Chicago: University of Chicago Press Zbignlew, M (1992) Genetic algorithms + data structures = evolution programs Berlin: SpringerVerlag

distribution V Figure 71 shows this definition of error in graphical form The concepts c and h are depicted by the sets of instances within X that they label as positive The error of h with respect to c is the probability that a randomly drawn instance will fall into the region where h and c disagree (ie, their set difference) Note we have chosen to define error over the entire distribution of instances-not simply over the training examples-because this is the true error we expect to encounter when actually using the learned hypothesis h on subsequent instances drawn from D Note that error depends strongly on the unknown probability distribution 2 For example, if D is a uniform probability distribution that assigns the same ) probability to every instance in X, then the error for the hypothesis in Figure 71 will be the fraction of the total instance space that falls into the region where h and c disagree However, the same h and c will have a much higher error if D happens to assign very high probability to instances for which h and c disagree In the extreme, if V happens to assign zero probability to the instances for which

Don t forget that visit to Interface Builder to change the binding of your Slider and Text Field controls, or you will surely see an exception thrown when you run the program. If you now Build and Run the program will behave correctly: the color well changes smoothly as the Slider or Text Field values change.

FIGURE 71 The error of hypothesis h with respect to target concept c The error of h with respect to c is the probability that a randomly drawn instance will fall into the region where h and c disagree on its classification The + and - points indicate positive and negative training examples Note h has a nonzero error with respect to c despite the fact that h and c agree on all five training examples observed thus far

h ( x ) = ~ ( x ) , the error for the h in Figure 71 will be 1, despite the fact the then h and c agree on a very large number of (zero probability) instances

As you have probably come to expect given the flexibility of Xcode s Developer Tools, there are several ways to use debugger features in Xcode. Overall you have four approaches to debugging: The Code Editor The Debugger window The Mini Debugger The Console view You have some flexibility in choosing which debugging approach Xcode uses. In Xcode s Debugging Preferences, the drop-down list labeled On Start allows you to select