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fDesc[0] = "This work is an attempt to overcome the use of greedy heuristics and search the decision tree space in a natural way. More specifically, we make use of genetic algorithms to directly evolve binary decision trees in the conquest for the one that most closely matches the target concept. <br /> <br /> On doing so we adopt a natural representation of the search space using actual decision trees and not binary strings. We couple our objective with a simplification motivation. We use GAs to robustly evolve accurate as well as simple decision trees.<br /> <br /> Main features:<br /> - GATree can continue decision tree evolution for as long as needed. If we have ample resources then we can expect an increasingly best-fit decision tree. Also, we can stop the evolution whenever the results are satisfactory since we evolve complete solutions to the problem.<br /> <br /> - GAtree allows the user to select the characteristics of the resulting decision tree. Its easy to prefer smaller or more accurate trees.<br /> <br /> - GAtree can provide a set of totally different decision trees that are close matches to the solution space. All those trees can be used alternatively to the best-fit one.<br /> <br /> - There are certain domains where statistical inducers can not produce optimal trees. GATree can overcome global or local minimums. Please read the papers that present this approach and its benefits.";

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