Impact of Crossover Bias in Genetic Programming

McPhee, Nicholas Freitag; Dramdahl, M. Kirbie; Donatucci, David

doi:10.1145/2739480.2754778

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…Previous work has already shown that the percentage of ancestor individuals having descendants in the last generation is low and that only a small fraction of ancestors contribute genetic material to the solutions in the last generation (Donatucci et al 2014;McPhee et al 2015McPhee et al , 2016. We quantify the fraction of contributing ancestors as the ratio between the size of the trace graph (in which vertices represent ancestors that have contributed a part of their genotype to their descendants in the final population) and the size of the genealogy graph.…”

Section: Contribution Ratiomentioning

confidence: 99%

Population diversity and inheritance in genetic programming for symbolic regression

2023

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In this work we aim to empirically characterize two important dynamical aspects of GP search: the evolution of diversity and the propagation of inheritance patterns. Diversity is calculated at the genotypic and phenotypic levels using efficient similarity metrics. Inheritance information is obtained via a full genealogical record of evolution as a directed acyclic graph and a set of methods for extracting relevant patterns. Advances in processing power enable our approach to handle previously infeasible graph sizes of millions of arcs and vertices. To enable a more comprehensive analysis we employ three closely-related but different evolutionary models: canonical GP, offspring selection and age-layered population structure. Our analysis reveals that a relatively small number of ancestors are responsible for producing the majority of descendants in later generations, leading to diversity loss. We show empirically across a selection of five benchmark problems that each configuration is characterized by different rates of diversity loss and different inheritance patterns, in support of the idea that each new problem may require a unique approach to solve optimally.

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Section: Contribution Ratiomentioning

confidence: 99%

Population diversity and inheritance in genetic programming for symbolic regression

2023

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“…The constrained dimensionally aware GP was designed in [56] to ensure only semantically correct individuals can be generated to improve the interpretability of evolved rules for JSS. The crossover bias for having the more fit parent as the root parent was presented in [57]. These methods tend to achieve the goal by utilising the semantics of GP individuals during the evolutionary process.…”

Section: Related Work On Genetic Operators Of Gpmentioning

confidence: 99%

Correlation Coefficient-Based Recombinative Guidance for Genetic Programming Hyperheuristics in Dynamic Flexible Job Shop Scheduling

Zhang

Mei

Nguyen

et al. 2021

IEEE Trans. Evol. Computat.

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Dynamic flexible job shop scheduling is a challenging combinatorial optimisation problem due to its complex environment. In this problem, machine assignment and operation sequencing decisions need to be made simultaneously under the dynamic environments. Genetic programming, as a hyper-heuristic approach, has been successfully used to evolve scheduling heuristics for dynamic flexible job shop scheduling. However, in traditional genetic programming, recombination between parents may disrupt the beneficial building-blocks by choosing the crossover points randomly. This paper proposes a recombinative mechanism to provide guidance for genetic programming to realise effective and adaptive recombination for parents to produce offspring. Specifically, we define a novel measure for the importance of each subtree of an individual, and the importance information is utilised to decide the crossover points. The proposed recombinative guidance mechanism attempts to improve the quality of offspring by preserving the promising building-blocks of one parent and incorporating good buildingblocks from the other. The proposed algorithm is examined on six scenarios with different configurations. The results show that the proposed algorithm significantly outperforms the state-of-theart algorithms on most tested scenarios, in terms of both final test performance and convergence speed. In addition, the rules obtained by the proposed algorithm have good interpretability.

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“…The constrained dimensionally aware GP was designed based on the types of features in [153] to ensure only semantically correct individuals can be generated to improve the interpretability of evolved rules for JSS. The crossover bias for having the more fit parent as the root parent was presented in [151]. These approaches tend to achieve the goal by utilising the semantics of GP individuals during the evolutionary process.…”

Section: Semantic Crossovermentioning

confidence: 99%

Genetic Programming Hyper-heuristics for Dynamic Flexible Job Shop Scheduling

Zhang¹

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Dynamic flexible job shop scheduling (DFJSS) has received widespread attention from academia and industry due to its reflection in real-world scheduling applications such as order picking in the warehouse and the manufacturing industry. It requires complex routing and sequencing decisions under unpredicted dynamic events. Genetic programming, as ahyper-heuristic approach (GPHH), has been successfully applied to evolve scheduling heuristics for DFJSS automatically due to its flexible representation. Although GPHH has achieved certain success in solving the DFJSS problems, there are still some limitations for applying GPHH to DFJSS, particularly in terms of its training efficiency, large search space, search mechanism, and multitask solving ability. The overall goal of this thesis is to develop effective GPHH algorithmsto evolve scheduling heuristics for DFJSS efficiently. Different machine learning techniques, i.e., surrogate, feature selection, specialised genetic operator, and multitask learning, are incorporated in this thesis to tackle the limitations. First, this thesis develops a novel multi-fidelity based surrogate-assisted GPHH for DFJSS to improve the training efficiency of GPHH. Specifically, multi-fidelity based surrogate models are first designed by simplifying the problem to be solved. Then, an effective collaboration mechanism with knowledge transfer is proposed for utilising the advantages of the multifidelity based surrogate models to solve the problem. The results show that the proposed algorithm can dramatically reduce the computational cost of GPHH without sacrificing the performance in all the test scenarios. With the same training time, the proposed algorithm can achieve significantly better performance than its counterparts in most scenarios while no worse in others. Second, this thesis designs a novel two-stage GPHH framework with feature selection to evolve scheduling heuristics for DFJSS automatically. Based on this framework, this thesis further proposes to evolve scheduling heuristics with only the selected features by eliminating the unselected features properly. Specifically, individual adaptation strategies are proposed to generate individuals with only the selected features by utilising the information of both the selected features and the investigated individuals during the feature selection process. The results show that the proposed algorithm can successfully achieve scheduling heuristics with fewer unique features and smaller sizes, which tends to be more interpretable. In addition, the proposed algorithm can evolve comparable scheduling heuristic with that obtained by the traditional GPHH within a much shorter training time. Third, this thesis proposes a novel recombinative mechanism to provide guidance for GPHH based on the importance of subtrees to realise effective and adaptive recombination for parents to produce offspring. Two measures are proposed to measure the importance of all the subtrees of an individual. The first one is based on the frequency of features, and the second is based on the correlation between the behaviour of subtrees and the whole tree (i.e., an individual). The importance information is utilised to decide the crossover points for the parents. The proposed recombinative guidance mechanism attempts to improve the quality of offspring by preserving the promising building-blocks of one parent and incorporating good building-blocks from the other. The results show that the proposed algorithm based on the correlation importance measure performs better than the proposed algorithm based on the feature frequency importance measure. In addition, the proposed algorithm based on the correlation importance measure between the behaviour of subtrees significantly also outperforms the state-of-the-art algorithms on most tested scenarios. Last, this thesis proposes a multitask GPHH approach and a surrogate-assisted multitask GPHH approach to solving multiple DFJSS tasks simultaneously. First, an effective hyper-heuristic multitask algorithm is proposed by adapting the traditional evolutionary multitask algorithms based on the characteristics of GPHH. Second, this thesis develops a novel surrogate-assisted multitask GPHH approach to solving multiple DFJSS tasks by sharing useful knowledge between different DFJSS scheduling tasks. Specifically, the surrogate-assisted multitask GPHH algorithm employs the phenotypic characterisation technique to measure the behaviours of scheduling rules to build a surrogate for each task accordingly. The built surrogates are not only used to improve the efficiency of solving each single DFJSS task but also utilised for knowledge sharing between multiple DFJSS tasks in multitask learning. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for all the test scenarios. The results also observe that the proposed algorithms manage to solve multiple tasks collaboratively in terms of the evolved scheduling heuristics for different tasks in a multitask scenario.

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Impact of Crossover Bias in Genetic Programming

Cited by 9 publications

References 24 publications

Population diversity and inheritance in genetic programming for symbolic regression

Population diversity and inheritance in genetic programming for symbolic regression

Correlation Coefficient-Based Recombinative Guidance for Genetic Programming Hyperheuristics in Dynamic Flexible Job Shop Scheduling

Genetic Programming Hyper-heuristics for Dynamic Flexible Job Shop Scheduling

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