Parameter Sensitivity Patterns in the Plant Propagation Algorithm

Jonge, Marleen de; Berg, Daan van den

doi:10.5220/0010134300920099

Cited by 6 publications

(8 citation statements)

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“…All 14 functions resulting in these 59 instances can be explicitly found in Table 1 (and some in Figure 1), each with its global minimum and domain of definition. It should be noted that this suite is identical to many other PPA-studies [9,10,34,[40][41][42], facilitating connections beyond this study alone, and abiding by 'best practices' in benchmarking [2]. Each run counts 50,000 function evaluations and is repeated 15 times, for all 8 selection methods, on all 59 function instances, resulting in 7080 runs with a total of 354 million function evaluations for the entire experiment.…”

Section: Methodsmentioning

confidence: 99%

“…Luckily though, PPA has relatively few parameters, and some progress has already been made. Work by Marleen de Jonge showed that PPA is mostly insensitive to variations in its combined parameter values for population size (𝑝𝑜𝑝𝑆𝑖𝑧𝑒) and maximum number of offspring per parent (𝑛 𝑚𝑎𝑥 ) when deployed on a continuous benchmark function instance [9,10]. A very curious result from these studies is that this parameter sensitivity of PPA, though largely constant for any specific benchmark function instance, changes when the dimensionality increases.…”

Section: Introductionmentioning

confidence: 99%

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Survivor selection in a crossoverless evolutionary algorithm

Brouwer

Dijkzeul

Koppenhol

et al. 2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Survivor selection in a crossoverless evolutionary algorithm

Brouwer

Dijkzeul

Koppenhol

et al. 2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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“…It can be applied to a broad spectrum of continuous, discrete and mixed objective landscapes in scientific, industrial and even artistic optimization problems [12, 14-16, 18, 30, 33, 34, 47]. A most recent investigation suggested that one version of the algorithm might be largely parameter independent [10,11].…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

“…The 27 Hamiltonian graphs on the plateau can be thought of as a network 10 with problem instances embedded in its nodes, and its links symbolizing a 1-bit mutation (edgeinsert or edge-removal). This plateau network is connected, and has 64 links between its 27 nodes with connection degrees ranging from 2 to 7.…”

Section: The Hamiltonian Plateaumentioning

confidence: 99%

The Hardest Hamiltonian Cycle Problem Instances: The Plateau of Yes and the Cliff of No

Sleegers

Berg

2022

SN COMPUT. SCI.

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We use two evolutionary algorithms to make hard instances of the Hamiltonian cycle problem. Hardness (or ‘fitness’), is defined as the number of recursions required by Vandegriend–Culberson, the best known exact backtracking algorithm for the problem. The hardest instances, all non-Hamiltonian, display a high degree of regularity and scalability across graph sizes. These graphs are found multiple times through independent runs, and by both evolutionary algorithms, suggesting the search space might contain monotonic paths towards the global maximum. For Hamiltonian-bound evolution, some hard graphs were found, but convergence is much less consistent. In this extended paper, we survey the neighbourhoods of both the hardest yes- and no-instances produced by the evolutionary algorithms. Results show that the hardest no-instance resides on top of a steep cliff, while the hardest yes-instance turns out to be part of a plateau of 27 equally hard instances. While definitive answers are far away, the results provide a lot of insight in the Hamiltonian cycle problem’s state space.

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“…Marleen de Jonge investigated simultaneous 𝑝𝑜𝑝𝑆𝑖𝑧𝑒 and 𝑚𝑎𝑥𝑂 𝑓 𝑓 𝑠𝑝𝑟𝑖𝑛𝑔 settings for broad ranges of continuous benchmark functions. Although the earliest pilot suggested a 'window of optimality' [15], a later and more comprehensive study concluded that PPA is unsensitive to settings -but only on a single benchmark function instance. Even just changing the dimensionality gave rise to different and non-trivial results [14].…”

Section: Ppa and Parametersmentioning

confidence: 99%