SGP-DT: Semantic Genetic Programming Based on Dynamic Targets
Stefano Ruberto, Valerio Terragni and Jason Moore
Semantic GP is a promising approach that introduces semantic awareness during genetic evolution. This paper presents a new approach called Semantic Genetic Programming based on Dynamic Target (SGP-DT) that divides the search problem into multiple GP runs. The evolution in each run is guided by a new (dynamic) target based on the residual errors. To obtain the final solution, SGP-DT combines the solutions of each run using linear scaling. SGP-DT proposes a new approach to exchange functionalities between individuals without relying on the classic formulations of crossovers. The synergy between linear scaling and mutation yields to final solutions with remarkable performances both in approximation errors and computational cost. We valuate SGP-DT on eight well known data sets and compare with e-lexicase, a state-of-the-art evolutionary technique. SGP-DT achieves small RMSE values, on average 23.19 % smaller than the one of e-lexicase.
Effect of Parent Selection Methods on Modularity
Anil Saini and Lee Spector
The effects of various genetic operators and parent selection algorithms on the performance of a genetic programming system on different problems have been well studied. In this paper, we analyze how different selection algorithms influence modularity in the population of evolving programs. In particular, we observe how the number of individuals with some form of modular structure changes over generations for various selection algorithms.
An Evolutionary View on Reversible Shift-invariant Transformations
Luca Mariot, Stjepan Picek, Domagoj Jakobovic and Alberto Leporati
We consider the problem of evolving a particular kind of shift-invariant transformation — namely, Reversible Cellular Automata (RCA) defined by conserved landscape rules — using GA and GP. To this end, we employ three different optimization strategies: a single-objective approach carried out with GA and GP where only the reversibility constraint of marker CA is considered, a multi-objective approach based on GP where both reversibility and the Hamming weight are taken into account, and a lexicographic approach where GP first optimizes only the reversibility property until a conserved landscape rule is obtained, and then maximizes the Hamming weight while retaining reversibility. The results are discussed in the context of three different research questions stemming from exhaustive search experiments on conserved landscape CA, which concern 1) the difficulty of the associated optimization problem for GA and GP, 2) the utility of conserved landscape CA in the domain of cryptography and reversible computing, and 3) the relationship between the reversibility property and the Hamming weight.