Dynamic GP fitness cases in static and dynamic optimisation problems

Galván, Edgar; Vázquez-Mendoza, Lucia; Schoenauer, Marc; Trujillo, Leonardo

doi:10.1145/3067695.3076055

Cited by 7 publications

(3 citation statements)

References 1 publication

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The range of problem domains for GP are wide and this form of EA has been found to be beneficial for problems with multiple local optima and for problems with a varying degree of complexity [9], making EAs ideal for highly complex problems including the automatic configuration of deep neural networks' architectures and their training (an indepth recent literature review in this emerging research area can be found in [8]). However despite the well documented effectiveness of canonical GP, there are well-known limitations of these methods, through the study of properties of encodings [17], [18], and research is on going into finding and developing approaches to improve their overall performance including promoting neutrality in deceptive and challenging landscapes [16], [20], [38], [39], dynamic fitness cases [23], [24], reuse of code [21], variants of GP [13], use of surrogate models [27], [28] to mention a few examples. Successful examples of these improvements can be found in applicable areas including energy-based problems [15], [22].…”

Section: Related Workmentioning

confidence: 99%

Promoting Semantics in Multi-objective Genetic Programming based on Decomposition

Galván,

Stapleton

2020

Preprint

View full text Add to dashboard Cite

The study of semantics in Genetic Program (GP) deals with the behaviour of a program given a set of inputs and has been widely reported in helping to promote diversity in GP for a range of complex problems ultimately improving evolutionary search. The vast majority of these studies have focused their attention in single-objective GP, with just a few exceptions where Pareto-based dominance algorithms such as NSGA-II and SPEA2 have been used as frameworks to test whether highly popular semantics-based methods, such as Semantic Similarity-based Crossover (SSC), helps or hinders evolutionary search. Surprisingly it has been reported that the benefits exhibited by SSC in SOGP are not seen in Pareto-based dominance Multi-objective GP. In this work, we are interested in studying if the same carries out in Multi-objective Evolutionary Algorithms based on Decomposition (MOEA/D). By using the MNIST dataset, a well-known dataset used in the machine learning community, we show how SSC in MOEA/D promotes semantic diversity yielding better results compared to when this is not present in canonical MOEA/D.

show abstract

Section: Related Workmentioning

confidence: 99%

Promoting Semantics in Multi-objective Genetic Programming based on Decomposition

Galván,

Stapleton

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In this work, we use a distance, studied in the first author's works [1,3], that accounts for pairwise disagreements between two lists of ranked fitness values. We hope that these disagreements can inform us on whether an evolved population is useful in the face of a change.…”

Section: Kendall Tau Distancementioning

confidence: 99%

On the Use of Dynamic GP Fitness Cases in Static and Dynamic Optimisation Problems

Galván

Vázquez-Mendoza

Schoenauer

et al. 2018

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. In Genetic Programming (GP), the fitness of individuals is normally computed by using a set of fitness cases (FCs). Research on the use of FCs in GP has primarily focused on how to reduce the size of these sets. However, often, only a small set of FCs is available and there is no need to reduce it. In this work, we are interested in using the whole FCs set, but rather than adopting the commonly used GP approach of presenting the entire set of FCs to the system from the beginning of the search, referred as static FCs, we allow the GP system to build it by aggregation over time, named as dynamic FCs, with the hope to make the search more amenable. Moreover, there is no study on the use of FCs in Dynamic Optimisation Problems (DOPs). To this end, we also use the Kendall Tau Distance (KTD) approach, which quantifies pairwise dissimilarities among two lists of fitness values. KTD aims to capture the degree of a change in DOPs and we use this to promote structural diversity. Results on eight symbolic regression functions indicate that both approaches are highly beneficial in GP.

show abstract

“…including promoting neutrality in deceptive landscapes [25], [26], dynamic fitness cases [16], [17], to mention a few examples.…”

Section: Introductionmentioning

confidence: 99%

Semantic-based Distance Approaches in Multi-objective Genetic Programming

Galván¹,

Stapleton²

2020

Preprint

View full text Add to dashboard Cite

Semantics in the context of Genetic Program (GP) can be understood as the behaviour of a program given a set of inputs and has been well documented in improving performance of GP for a range of diverse problems. There have been a wide variety of different methods which have incorporated semantics into single-objective GP. The study of semantics in Multi-objective (MO) GP, however, has been limited and this paper aims at tackling this issue. More specifically, we conduct a comparison of three different forms of semantics in MOGP. One semanticbased method, (i) Semantic Similarity-based Crossover (SSC), is borrowed from single-objective GP, where the method has consistently being reported beneficial in evolutionary search. We also study two other methods, dubbed (ii) Semantic-based Distance as an additional criteriOn (SDO) and (iii) Pivot Similarity SDO. We empirically and consistently show how by naturally handling semantic distance as an additional criterion to be optimised in MOGP leads to better performance when compared to canonical methods and SSC. Both semantic distance based approaches made use of a pivot, which is a reference point from the sparsest region of the search space and it was found that individuals which were both semantically similar and dissimilar to this pivot were beneficial in promoting diversity. Moreover, we also show how the semantics successfully promoted in single-objective optimisation does not necessary lead to a better performance when adopted in MOGP.

show abstract

Dynamic GP fitness cases in static and dynamic optimisation problems

Cited by 7 publications

References 1 publication

Promoting Semantics in Multi-objective Genetic Programming based on Decomposition

Promoting Semantics in Multi-objective Genetic Programming based on Decomposition

On the Use of Dynamic GP Fitness Cases in Static and Dynamic Optimisation Problems

Semantic-based Distance Approaches in Multi-objective Genetic Programming

Contact Info

Product

Resources

About