
Computational Complexity Analysis of Genetic Programming
Genetic Programming (GP) is an evolutionary computation technique to sol...
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Solving evenparity problems using traceless genetic programming
A genetic programming (GP) variant called traceless genetic programming ...
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Multiplicative Drift Analysis
In this work, we introduce multiplicative drift analysis as a suitable w...
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GPRVM: Genetic Programingbased Symbolic Regression Using Relevance Vector Machine
This paper proposes a hybrid basis function construction method (GPRVM)...
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A divide and conquer method for symbolic regression
Symbolic regression aims to find a function that best explains the relat...
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LongTerm Evolution of Genetic Programming Populations
We evolve binary mux6 trees for up to 100000 generations evolving some ...
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Evolving Boolean Functions with Conjunctions and Disjunctions via Genetic Programming
Recently it has been proved that simple GP systems can efficiently evolve the conjunction of n variables if they are equipped with the minimal required components. In this paper, we make a considerable step forward by analysing the behaviour and performance of the GP system for evolving a Boolean function with unknown components, i.e., the function may consist of both conjunctions and disjunctions. We rigorously prove that if the target function is the conjunction of n variables, then the RLSGP using the complete truth table to evaluate program quality evolves the exact target function in O(ℓ n ^2 n) iterations in expectation, where ℓ≥ n is a limit on the size of any accepted tree. When, as in realistic applications, only a polynomial sample of possible inputs is used to evaluate solution quality, we show how RLSGP can evolve a conjunction with any polynomially small generalisation error with probability 1  O(^2(n)/n). To produce our results we introduce a supermultiplicative drift theorem that gives significantly stronger runtime bounds when the expected progress is only slightly superlinear in the distance from the optimum.
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