Exploring Genetic Programming Systems with MAP-Elites

Dolson, Emily; Lalejini, Alexander; Ofria, Charles

doi:10.1007/978-3-030-04735-1_1

Cited by 13 publications

(10 citation statements)

References 18 publications

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“…Up to now, most applications of MAP-Elites have been restricted to robotic tasks. However, ME has also been used in applications such as: developing neural networks for computer vision tasks [23], tackling the Workforce Scheduling, and Routing Problem (WRSP) [24] and enhancing Genetic Programming [62]. To the best of our knowledge, there is only one case where MAP-Elites has been applied for constrained optimization [29].…”

Section: Map-elites (Me)mentioning

confidence: 99%

Sequence-Based Selection Hyper-Heuristic Model via MAP-Elites

et al. 2021

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Combinatorial optimization problems (COPs) are paramount for real-life problems with discrete variables. Even though the number of combinations is finite, some problems exhibit exponential growth, rendering exact approaches unfeasible. So, approximate methods, such as heuristics, are customary for making fast decisions. Despite their small computational cost, each heuristic specializes in specific kinds of problems. Hence, other approaches have appeared for merging their strengths. One of these approaches is commonly known as selection hyper-heuristics. However, a trained hyper-heuristic usually carries the following problem: it provides scarce information about its sensitivity. This makes it more difficult to estimate the effect of changing a parameter. Illumination algorithms seek to improve this issue by focusing on exploration rather than on exploitation while preserving information about the best solutions with different criteria. Still, literature falls short when merging both approaches, representing a knowledge gap. Therefore, in this work, we test the feasibility of using an illumination algorithm, the MAP-Elites (ME), for tuning a sequence-based selection hyper-heuristic model for Balanced Partition problems. We choose ME since it has been successfully applied to a different COP, and by linking it with hyper-heuristics, we may exploit upon its benefits. So, we may achieve a hyper-heuristic that represents the best combination of heuristics. Simultaneously, we can gain information about the expected performance of diverse hyperheuristics, e.g., those that begin with a different heuristic. Our approach operates by creating a multidimensional map, where each design variable represents the application of a given heuristic. Afterward, ME generates mutated sequences and tests them to determine if they represent a better-performing solution. To test our approach, we consider 1500 instances that include easy and hard instances, analyzed under different scenarios. We also include limit instances that are neither easy nor hard. Our resulting data supports the feasibility of the proposed approach. We show that this kind of hyper-heuristic can perform toe-to-toe with a synthetic oracle. Besides, under some conditions, the former may even outperform the latter. This represents an outstanding result, since such an oracle can only be obtained by a brute-force approach, and because it demonstrates that merging both approaches (ME and hyper-heuristics) is a path worth pursuing. We also present how each parameter affects the model performance, concluding that some parameters can be critical while others are virtually irrelevant. This is also a relevant result, since it serves as the groundwork for future works so that they can focus on exploiting the most relevant parameters.

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Section: Map-elites (Me)mentioning

confidence: 99%

Sequence-Based Selection Hyper-Heuristic Model via MAP-Elites

et al. 2021

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“…In the third relevant group of approaches in the in-scope papers, methods based on linear GP are used to solve problems from the program synthesis benchmark suite [67], [68], [69], [70], [71]. Similar as in Assembler programming, the data is stored in registers and the provided functions operate on these registers.…”

Section: Linear Gp and Further Approachesmentioning

confidence: 99%

“…Similar as in Assembler programming, the data is stored in registers and the provided functions operate on these registers. E.g., the value stored in a register can be incremented or decremented by an Inc or Dec function, respectively, or a value can be explicitly stored in a specific register with a SetReg function [67]. As small changes in a genome may easily destroy the connection between functions and the related registers, Lalejini and Ofria [69] used a tag-based memory in their linear GP approach.…”

Section: Linear Gp and Further Approachesmentioning

confidence: 99%

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Recent Developments in Program Synthesis with Evolutionary Algorithms

Sobania¹,

Schweim²,

Rothlauf³

2021

Preprint

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The automatic generation of computer programs is one of the main applications with practical relevance in the field of evolutionary computation. With program synthesis techniques not only software developers could be supported in their everyday work but even users without any programming knowledge could be empowered to automate repetitive tasks and implement their own new functionality. In recent years, many novel program synthesis approaches based on evolutionary algorithms have been proposed and evaluated on common benchmark problems. Therefore, we identify in this work the relevant evolutionary program synthesis approaches and provide an in-depth analysis of their performance. The most influential approaches we identify are stack-based, grammar-guided, as well as linear genetic programming. Further, we find that these approaches perform well on benchmark problems if there is a simple mapping from the given input to the correct output. On problems where this mapping is complex, e.g., if the problem consists of several subproblems or requires iteration/recursion for a correct solution, results tend to be worse. Consequently, for future work, we encourage researchers not only to use a program's output for assessing the quality of a solution but also the way towards a solution (e.g., correctly solved sub-problems).

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“…In Logic-9, a solution comprises code that, when executed, performs the nine oneand two-input bitwise Boolean logic tasks (not, and, or, nand, orn, andn, nor, xor, and equ) (Ofria and Wilke, 2004;Dolson et al, 2019). To evaluate a program, we provide two numeric inputs that the program may use during its execution.…”

Section: Logic-9mentioning

confidence: 99%

What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?

Hernandez¹,

Lalejini²,

Dolson³

2021

Preprint

Self Cite

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It is generally accepted that "diversity" is associated with success in evolutionary algorithms. However, diversity is a broad concept that can be measured and defined in a multitude of ways. To date, most evolutionary computation research has measured diversity using the richness and/or evenness of a particular genotypic or phenotypic property. While these metrics are informative, we hypothesize that other diversity metrics are more strongly predictive of success. Phylogenetic diversity metrics are a class of metrics popularly used in biology, which take into account the evolutionary history of a population. Here, we investigate the extent to which 1) these metrics provide different information than those traditionally used in evolutionary computation, and 2) these metrics better predict the long-term success of a run of evolutionary computation. We find that, in most cases, phylogenetic metrics behave meaningfully differently from other diversity metrics. Moreover, our results suggest that phylogenetic diversity is indeed a better predictor of success.

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Exploring Genetic Programming Systems with MAP-Elites

Cited by 13 publications

References 18 publications

Sequence-Based Selection Hyper-Heuristic Model via MAP-Elites

Sequence-Based Selection Hyper-Heuristic Model via MAP-Elites

Recent Developments in Program Synthesis with Evolutionary Algorithms

What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?

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