Program bloat is a fundamental problem in the field of Genetic Programming (GP). Exponential growth of redundant and functionally useless sections of programs can quickly overcome a GP system, exhausting system resources and causing premature termination of the system before an acceptable solution can be found. Simplification is an attempt to remove such redundancies from programs. This paper looks at the effects of applying an algebraic simplification algorithm to programs during the GP evolution. The GP system with the simplification is examined and compared to a standard GP system on four regression and classification problems of varying difficulty. The results suggest that the GP system employing a simplification component can achieve superior efficiency and effectiveness to the standard system on these problems.
This paper investigates the effects on building blocks of using simplification in a Genetic Programming (GP) system to combat the problem of code bloat. The evolved genetic programs are simplified online during the evolutionary process using algebraic simplification rules and hashing techniques. A simplified form of building block (numerical-nodes) is tracked throughout individual GP runs both when using or not using online simplification of evolved genetic programs. The results suggest that online simplification disrupts existing potential building blocks during the evolution process. However, GP with simplification is capable of creating new building blocks which are used to form a more accurate solution, when compared to the standard GP. The effectiveness of GP systems utilising simplification can be correlated to the creation of these new building blocks.
This paper describes a genetic programming (GP) approach to medical data classification problems. In this approach, the evolved genetic programs are simplified online during the evolutionary process using algebraic simplification rules, algebraic equivalence and prime techniques. The new simplification GP approach is examined and compared to the standard GP approach on two medical data classification problems. The results suggest that the new simplification GP approach can not only be more efficient with slightly better classification performance than the basic GP system on these problems, but also significantly reduce the sizes of evolved programs. Comparison with other methods including decision trees, naive Bayes, nearest neighbour, nearest centroid, and neural networks suggests that the new GP approach achieved superior results to almost all of these methods on these problems. The evolved genetic programs are also easier to interpret than the ''hidden patterns'' discovered by the other methods.
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