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Semantics based crossover for boolean problems

Nguyen Quang Uy, Nguyen Xuan Hoai, Michael O'Neill, Bob McKay
2010 Proceedings of the 12th annual conference on Genetic and evolutionary computation - GECCO '10  
This paper investigates the role of semantic diversity and locality of crossover operators in Genetic Programming (GP) for Boolean problems.  ...  The experimental results show the positive effects both of promoting semantic diversity, and of improving semantic locality, in crossover operators.  ...  Acknowledgment This paper was funded under a Postgraduate Scholarship from the Irish Research Council for Science Engineering and Technology (IRCSET).  ... 
doi:10.1145/1830483.1830642 dblp:conf/gecco/UyHOM10 fatcat:atd23bhthvdpbf7nmfcu4pklvq

An Efficient Genetic Programming System with Geometric Semantic Operators and its Application to Human Oral Bioavailability Prediction [article]

Mauro Castelli, Luca Manzoni, Leonardo Vanneschi
2012 arXiv   pre-print
In fact, it outperforms standard genetic programming and a wide set of other well-known machine learning methods.  ...  Very recently new genetic operators, called geometric semantic operators, have been defined for genetic programming.  ...  In [22] the authors investigate the role of syntactic locality and semantic locality of crossover in GP.  ... 
arXiv:1208.2437v1 fatcat:abx3fgvgffbhfgjc45nhnz3jzq

SEMANTIC SEARCH TECHNIQUES FOR LEARNING SMALLER BOOLEAN EXPRESSION TREES IN GENETIC PROGRAMMING

NICHOLAS C. MILLER, PHILIP K. CHAN
2014 International Journal of Computational Intelligence and Applications  
One sub-field of Genetic Programming (GP) which has gained recent interest is semantic GP, in which programs are evolved by manipulating program semantics instead of program syntax.  ...  New crossover and mutation operators are introduced which address two of the major limitations of SGP: large program trees and reduced accuracy on high-arity problems.  ...  Second, a single point in the syntactic space represents a syntactically unique program, and corresponds with a single point in the semantic space (i.e. a program only has one behavior).  ... 
doi:10.1142/s1469026814500187 fatcat:r7axit3nnbc5fpckpyigigmcha

Comparison of semantic-based local search methods for multiobjective genetic programming

Tiantian Dou, Peter Rockett
2018 Genetic Programming and Evolvable Machines  
We report a series of experiments that use semantic-based local search within a multiobjective genetic programming (GP) framework.  ...  The depth fair selection strategy of Ito et al. is found to perform best compared with other subtree selection methods in the model refinement.  ...  distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were  ... 
doi:10.1007/s10710-018-9325-4 fatcat:5ztu2iab3vcs3gqc3rpbnzgxxe

Improving the Generalisation Ability of Genetic Programming with Semantic Similarity based Crossover [chapter]

Nguyen Quang Uy, Nguyen Thi Hien, Nguyen Xuan Hoai, Michael O'Neill
2010 Lecture Notes in Computer Science  
This paper examines the impact of semantic control on the ability of Genetic Programming (GP) to generalise via a semantic based crossover operator (Semantic Similarity based Crossover -SSC).  ...  The experimental results show that while using validation sets barely improve generalisation ability of GP, by using semantics, the performance of Genetic Programming is enhanced both on training and testing  ...  Acknowledgements This paper was funded under a Postgraduate Scholarship from the Irish Research Council for Science Engineering and Technology (IRCSET).  ... 
doi:10.1007/978-3-642-12148-7_16 fatcat:gb2p77h2gfbpnf4hujyr4ar7hq

Semantically-based crossover in genetic programming: application to real-valued symbolic regression

Nguyen Quang Uy, Nguyen Xuan Hoai, Michael O'Neill, R. I. McKay, Edgar Galván-López
2010 Genetic Programming and Evolvable Machines  
Abstract We investigate the effects of semantically-based crossover operators in Genetic Programming, applied to real-valued symbolic regression problems.  ...  These relations are used to guide variants of the crossover operator, resulting in two new crossover operators -Semantics Aware Crossover (SAC) and Semantic Similarity-based Crossover (SSC).  ...  Alternative Crossovers in Genetic Programming It is well-known that crossover is the primary operator in GP [35] .  ... 
doi:10.1007/s10710-010-9121-2 fatcat:zpgfngxf3zfebbmpfttwf3vziq

Locally geometric semantic crossover: a study on the roles of semantics and homology in recombination operators

Krzysztof Krawiec, Tomasz Pawlak
2012 Genetic Programming and Evolvable Machines  
This study presents an extensive account of Locally Geometric Semantic Crossover (LGX), a semantically-aware recombination operator for genetic programming (GP).  ...  LGX is designed to exploit the semantic properties of programs and subprograms, in particular the geometry of semantic space that results from distance-based fitness functions used predominantly in GP.  ...  Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s)  ... 
doi:10.1007/s10710-012-9172-7 fatcat:elm4iz3hbvfxhmqaajcmys3ynu

Grammar-based Genetic Programming: a survey

Robert I. McKay, Nguyen Xuan Hoai, Peter Alexander Whigham, Yin Shan, Michael O'Neill
2010 Genetic Programming and Evolvable Machines  
So it is not surprising that they have also become important as a method for formalizing constraints in Genetic Programming (GP).  ...  We trace their subsequent rise, surveying the various grammar-based formalisms that have been used in GP and discussing the contributions they have made to the progress of GP.  ...  Acknowledgment The authors thank Kwong Sak Leung, Man Leung Wong and Brian Ross for insightful discussions that helped to form their perspectives on grammar-based GP.  ... 
doi:10.1007/s10710-010-9109-y fatcat:rwdjrvwycrbmhl7dr6n5padrui

The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming

Mauro Castelli, Luca Manzoni, Luca Mariot, Giuliamaria Menara, Gloria Pietropolli
2022 Applied Sciences  
In recent years, a variant called geometric semantic genetic programming (GSGP) was successfully applied to many real-world problems.  ...  Among the evolutionary methods, one that is quite prominent is genetic programming.  ...  Geometric Semantic Genetic Programming In classical GP, the crossover (recombination) and mutation operators act on the genotype of the individual involved, usually modifying or changing parts of the subtrees  ... 
doi:10.3390/app12104836 fatcat:wb5p32hq3fdzxpblfiiip44gcy

The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming [article]

Mauro Castelli, Luca Manzoni, Luca Mariot, Giuliamaria Menara, Gloria Pietropolli
2022 arXiv   pre-print
Among the evolutionary methods, one that is quite prominent is Genetic Programming, and, in recent years, a variant called Geometric Semantic Genetic Programming (GSGP) has shown to be successfully applicable  ...  We show that a limited ability to use "old" generations is actually useful for the search process, thus showing a zero-cost way of improving the performances of GSGP.  ...  Geometric Semantic Genetic Programming In classical GP, the crossover (recombination) and mutation operators act on the genotype of the individual involved, usually modifying or changing parts of the subtrees  ... 
arXiv:2205.02598v1 fatcat:se3r5zo4r5df3cjyfb45m5e2t4

A Novel Framework for Semantic OrientedAbstractive Text Summarization

N. Moratanch, S. Chitrakala
2019 Journal of Web Engineering  
The contribution of our works are Joint Model Predicate Sense Disambiguation and Semantic Role Labelling termed as Joint (PSD+SRL) is proposed to better capture the semantic representation of text.  ...  The content selection involves semantic based content selection and feature extraction are selected by Genetic Algorithm.  ...  The objective of semantic role labelling is to determine the syntactic constituents of a sentence with respect to its predicate and identifing the semantic roles played such as Agent, Direct and Indirect  ... 
doi:10.13052/jwe1540-9589.1784 fatcat:62zfycbebndrtpja2iqzdragpu

Why evolution is not a good paradigm for program induction

John R. Woodward, Ruibin Bai
2009 Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC '09  
We revisit the roots of Genetic Programming (i.e.  ...  Natural Evolution), and conclude that the mechanisms of the process of evolution (i.e. selection, inheritance and variation) are highly suited to the process; genetic code is an effective transmitter of  ...  In biological crossover the amount of genetic material is conserved, and this is often the case with crossover operators proposed in GP.  ... 
doi:10.1145/1543834.1543915 dblp:conf/gecco/WoodwardB09a fatcat:o7nvmj5sqvcqpo2fpyq7t2cdau

Emergent computation: Self-organizing, collective, and cooperative phenomena in natural and artificial computing networks

Stephanie Forrest
1990 Physica D : Non-linear phenomena  
Emergent intelligence de-emphasizes the role of explicit knowledge and encourages the development of solutions that incorporate the task description as a component of the problem solver.  ...  This allows the constraints of the task to be represented more naturally and permits only pertinent task specific knowledge to emerge in the course of solving the problem.  ...  In actuality, GP's use of a crossover operator that preserves the syntactic constraints of the representation language is the only true distinction between standard genetic algorithms and genetic programming  ... 
doi:10.1016/0167-2789(90)90063-u fatcat:2zjxwx637zfx7jl7iyux6g6biu

Genetic Programming: A Review of Some Concerns [chapter]

Maumita Bhattacharya, Baikunth Nath
2001 Lecture Notes in Computer Science  
In other words GP seemingly holds the key to attain the goal of "automated program generation".  ...  Genetic Programming (GP) is gradually being accepted as a promising variant of Genetic Algorithm (GA) that evolves dynamic hierarchical structures, often described as programs.  ...  Poli and Langdon argue that the fraction of genetic material changed by crossover is smaller in longer programs [19] .  ... 
doi:10.1007/3-540-45718-6_109 fatcat:kv4lmzeohzct3oo2jjzte7m4t4

The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems [chapter]

Julio Albinati, Gisele L. Pappa, Fernando E. B. Otero, Luiz Otávio V. B. Oliveira
2015 Lecture Notes in Computer Science  
First, it analyses the impact of using Manhattan and Euclidean distance geometric semantic crossovers in the learning process.  ...  The results show that the use of different distance functions in the semantic geometric crossover has little impact on the test error, and that our optimized crossover masks yield slightly better results  ...  Introduction The development of methods that take the semantics of the solutions being evolved into account is a trend in the genetic programming community, with special attention given to methods based  ... 
doi:10.1007/978-3-319-16501-1_1 fatcat:7ntqjneedze3hpr2qmqaeczxpu
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