1,234 research outputs found
The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems
This paper investigates the impact of geometric semantic crossover operators in a wide range of symbolic regression problems. First, it analyses the impact of using Manhattan and Euclidean distance geometric semantic crossovers in the learning process. Then, it proposes two strategies to numerically optimize the crossover mask based on mathematical properties of these operators, instead of simply generating them randomly. An experimental analysis comparing geometric semantic crossovers using Euclidean and Manhattan distances and the proposed strategies is performed in a test bed of twenty datasets. 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. For SGP practitioners, we suggest the use of the semantic crossover based on the Euclidean distance, as it achieved similar results to those obtained by more complex operators
Semantically-based crossover in genetic programming: application to real-valued symbolic regression
We investigate the effects of semantically-based crossover operators in genetic programming, applied to real-valued symbolic regression problems. We propose two new relations derived from the semantic distance between subtrees, known as semantic equivalence and semantic similarity. 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). SAC, was introduced and previously studied, is added here for the purpose of comparison and analysis. SSC extends SAC by more closely controlling the semantic distance between subtrees to which crossover may be applied. The new operators were tested on some real-valued symbolic regression problems and compared with standard crossover (SC), context aware crossover (CAC), Soft Brood Selection (SBS), and No Same Mate (NSM) selection. The experimental results show on the problems examined that, with computational effort measured by the number of function node evaluations, only SSC and SBS were significantly better than SC, and SSC was often better than SBS. Further experiments were also conducted to analyse the perfomance sensitivity to the parameter settings for SSC. This analysis leads to a conclusion that SSC is more constructive and has higher locality than SAC, NSM and SC; we believe these are the main reasons for the improved performance of SSC
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
Geometric Semantic Grammatical Evolution
This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.Geometric Semantic Genetic Programming (GSGP) is a novel form of
Genetic Programming (GP), based on a geometric theory of evolutionary algorithms,
which directly searches the semantic space of programs. In this chapter,
we extend this framework to Grammatical Evolution (GE) and refer to the new
method as Geometric Semantic Grammatical Evolution (GSGE). We formally derive
new mutation and crossover operators for GE which are guaranteed to see a simple
unimodal fitness landscape. This surprising result shows that the GE genotypephenotype
mapping does not necessarily imply low genotype-fitness locality. To
complement the theory, we present extensive experimental results on three standard
domains (Boolean, Arithmetic and Classifier)
A Study of Geometric Semantic Genetic Programming with Linear Scaling
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceMachine Learning (ML) is a scientific discipline that endeavors to enable computers
to learn without the need for explicit programming. Evolutionary Algorithms (EAs),
a subset of ML algorithms, mimic Darwin’s Theory of Evolution by using natural
selection mechanisms (i.e., survival of the fittest) to evolve a group of individuals
(i.e., possible solutions to a given problem). Genetic Programming (GP) is the most
recent type of EA and it evolves computer programs (i.e., individuals) to map a set of
input data into known expected outputs. Geometric Semantic Genetic Programming
(GSGP) extends this concept by allowing individuals to evolve and vary in the semantic
space, where the output vectors are located, rather than being constrained by syntaxbased
structures. Linear Scaling (LS) is a method that was introduced to facilitate the
task of GP of searching for the best function matching a set of known data. GSGP
and LS have both, independently, shown the ability to outperform standard GP for
symbolic regression. GSGP uses Geometric Semantic Operators (GSOs), different
from the standard ones, without altering the fitness, while LS modifies the fitness
without altering the genetic operators. To the best of our knowledge, there has been
no prior utilization of the combined methodology of GSGP and LS for classification
problems. Furthermore, despite the fact that they have been used together in one
practical regression application, a methodological evaluation of the advantages and
disadvantages of integrating these methods for regression or classification problems
has never been performed. In this dissertation, a study of a system that integrates both
GSGP and LS (GSGP-LS) is presented. The performance of the proposed method, GSGPLS,
was tested on six hand-tailored regression benchmarks, nine real-life regression
problems and three real-life classification problems. The obtained results indicate that
GSGP-LS outperforms GSGP in the majority of the cases, confirming the expected
benefit of this integration. However, for some particularly hard regression datasets,
GSGP-LS overfits training data, being outperformed by GSGP on unseen data. This
contradicts the idea that LS is always beneficial for GP, warning the practitioners about
its risk of overfitting in some specific cases.A Aprendizagem Automática (AA) é uma disciplina científica que se esforça por
permitir que os computadores aprendam sem a necessidade de programação explícita.
Algoritmos Evolutivos (AE),um subconjunto de algoritmos de ML, mimetizam a Teoria
da Evolução de Darwin, usando a seleção natural e mecanismos de "sobrevivência dos
mais aptos"para evoluir um grupo de indivíduos (ou seja, possíveis soluções para
um problema dado). A Programação Genética (PG) é um processo algorítmico que
evolui programas de computador (ou indivíduos) para ligar características de entrada e
saída. A Programação Genética em Geometria Semântica (PGGS) estende esse conceito
permitindo que os indivíduos evoluam e variem no espaço semântico, onde os vetores
de saída estão localizados, em vez de serem limitados por estruturas baseadas em
sintaxe. A Escala Linear (EL) é um método introduzido para facilitar a tarefa da PG de
procurar a melhor função que corresponda a um conjunto de dados conhecidos. Tanto
a PGGS quanto a EL demonstraram, independentemente, a capacidade de superar a
PG padrão para regressão simbólica. A PGGS usa Operadores Semânticos Geométricos
(OSGs), diferentes dos padrões, sem alterar o fitness, enquanto a EL modifica o fitness
sem alterar os operadores genéticos. Até onde sabemos, não houve utilização prévia
da metodologia combinada de PGGS e EL para problemas de classificação. Além disso,
apesar de terem sido usados juntos em uma aplicação prática de regressão, nunca foi
realizada uma avaliação metodológica das vantagens e desvantagens da integração
desses métodos para problemas de regressão ou classificação. Nesta dissertação, é
apresentado um estudo de um sistema que integra tanto a PGGS quanto a EL (PGGSEL).
O desempenho do método proposto, PGGS-EL, foi testado em seis benchmarks de
regressão personalizados, nove problemas de regressão da vida real e três problemas
de classificação da vida real. Os resultados obtidos indicam que o PGGS-EL supera
o PGGS na maioria dos casos, confirmando o benefício esperado desta integração.
No entanto, para alguns conjuntos de dados de regressão particularmente difíceis, o
PGGS-EL faz overfit aos dados de treino, obtendo piores resultados em comparação com
PGGS em dados não vistos. Isso contradiz a ideia de que a EL é sempre benéfica para
a PG, alertando os praticantes sobre o risco de overfitting em alguns casos específicos
Semantically-Oriented Mutation Operator in Cartesian Genetic Programming for Evolutionary Circuit Design
Despite many successful applications, Cartesian Genetic Programming (CGP)
suffers from limited scalability, especially when used for evolutionary circuit
design. Considering the multiplier design problem, for example, the 5x5-bit
multiplier represents the most complex circuit evolved from a randomly
generated initial population. The efficiency of CGP highly depends on the
performance of the point mutation operator, however, this operator is purely
stochastic. This contrasts with the recent developments in Genetic Programming
(GP), where advanced informed approaches such as semantic-aware operators are
incorporated to improve the search space exploration capability of GP. In this
paper, we propose a semantically-oriented mutation operator (SOMO) suitable for
the evolutionary design of combinational circuits. SOMO uses semantics to
determine the best value for each mutated gene. Compared to the common CGP and
its variants as well as the recent versions of Semantic GP, the proposed method
converges on common Boolean benchmarks substantially faster while keeping the
phenotype size relatively small. The successfully evolved instances presented
in this paper include 10-bit parity, 10+10-bit adder and 5x5-bit multiplier.
The most complex circuits were evolved in less than one hour with a
single-thread implementation running on a common CPU.Comment: Accepted for Genetic and Evolutionary Computation Conference (GECCO
'20), July 8--12, 2020, Canc\'un, Mexic
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