Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
A Mathematical Analysis of Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Problem Difficulty and Code Growth in Genetic Programming
Genetic Programming and Evolvable Machines
Automatic Selection Pressure Control in Genetic Programming
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
An analysis of constructive crossover and selection pressure in genetic programming
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A comparison of selection schemes used in evolutionary algorithms
Evolutionary Computation
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
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In this paper, we develop a new fitness function based on adjustment of the original fitness function using population performance. We call this new fitness function norm-referenced fitness function since it is motivated by the idea of norm-referenced test. Experiments performed in two benchmark problems show that, the norm-referenced fitness function developed is capable of improving the overall performance of GP system. Further analysis of the fitness function reveals that the original fitness function suffers from an implicit bias we named as implicit bias towards exploitation in later generations. This implicit bias pushes the population towards convergence. The norm-referenced fitness developed however does not inherit this bias, and we think this is the main reason why the norm-referenced fitness function is able to outperform the original fitness function. We further study the selection of the newly introduced parameter lambda in norm-referenced fitness function and give a number of advices to select the optimal value of the parameter.