The Design of Evolutionary Algorithms: A Computer Science Perspective on the Compatibility of Evolution and Design

Jeavons, Peter

doi:10.1111/zygo.12840

Cited by 3 publications

(3 citation statements)

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“…In his response to Kojonen's account, Jeavons (2022) focuses on Kojonen's argument that biological fitness landscapes were fine-tuned so that evolutionary searches would achieve predetermined outcomes. Although Jeavons (2022) does not frame it as such, his insightful analysis of evolutionary algorithms poses severe challenges to Kojonen's premise that the landscapes could have been sufficiently fine-tuned solely due to fine-tuning of the laws of physics and the universe's initial conditions. Jeavons (2022) states that "additional feedback mechanisms must generally be added to modify the properties of the evolutionary algorithm in a goal-directed way during a run, based on some information about its current performance".…”

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confidence: 99%

“…Although Jeavons (2022) does not frame it as such, his insightful analysis of evolutionary algorithms poses severe challenges to Kojonen's premise that the landscapes could have been sufficiently fine-tuned solely due to fine-tuning of the laws of physics and the universe's initial conditions. Jeavons (2022) states that "additional feedback mechanisms must generally be added to modify the properties of the evolutionary algorithm in a goal-directed way during a run, based on some information about its current performance". He then states that the adjustments must be based on such variables as the current population and individuals' current fitness.…”

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confidence: 99%

“…Instead, multiple infusions of new information would have been required to continuously tailor evolutionary searches to achieve desired outcomes. Jeavons (2022) states, "to achieve significant results, an evolutionary algorithm must be carefully tailored to the problem in hand, and the problem itself must have appropriate properties". Somewhat curiously, neither Kojonen (2022b) nor Jeavon himself acknowledge or solve these difficulties for Kojonen's model.…”

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On the Relationship between Design and Evolution

et al. 2023

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A longstanding question in science and religion is whether standard evolutionary models are compatible with the claim that the world was designed. In The Compatibility of Evolution and Design, theologian E. V. Rope Kojonen constructs a powerful argument that not only are evolution and design compatible, but that evolutionary processes (and biological data) strongly point to design. Yet Kojonen’s model faces several difficulties, each of which raise hurdles for his understanding of how evolution and design can be harmonized. First, his argument for design (and its compatibility with evolution) relies upon a particular view of nature in which fitness landscapes are “fine-tuned” to allow proteins to evolve from one form to another by mutation and selection. But biological data run contrary to this claim, which poses a problem for Kojonen’s design argument (and, as such, his attempt to harmonize design with evolution). Second, Kojonen appeals to the bacterial flagellum to strengthen his case for design, yet the type of design in the flagellum is incompatible with mainstream evolutionary theory, which (again) damages his reconciliation of design with evolution. Third, Kojonen regards convergent evolution as notable positive evidence in favor of his model (including his version of design), yet convergent evolution actually harms the justification of common ancestry, which Kojonen also accepts. This, too, mars his reconciliation of design and evolution. Finally, Kojonen’s model damages the epistemology that undergirds his own design argument as well as the design intuitions of everyday “theists on the street”, whom he seeks to defend. Thus, despite the remarkable depth, nuance, and erudition of Kojonen’s account, it does not offer a convincing reconciliation of ‘design’ and ‘evolution’.

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On the Relationship between Design and Evolution

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Optimization of breeding program design through stochastic simulation with evolutionary algorithms

Hassanpour,

Geibel,

Simianer

et al. 2024

G3: Genes, Genomes, Genetics

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The effective planning and allocation of resources in modern breeding programs is a complex task. Breeding program design and operational management have a major impact on the success of a breeding program and changing parameters such as the number of selected/phenotyped/genotyped individuals in the breeding program will impact genetic gain, genetic diversity, and costs. As a result, careful assessment and balancing of design parameters is crucial, taking into account the trade-offs between different breeding goals and associated costs. In a previous study, we optimized the resource allocation strategy in a dairy cattle breeding scheme via the combination of stochastic simulations and kernel regression, aiming to maximize a target function containing genetic gain and the inbreeding rate under a given budget. However, the high number of simulations required when using the proposed kernel regression method to optimize a breeding program with many parameters weakens the effectiveness of such a method. In this work, we are proposing an optimization framework that builds on the concepts of kernel regression but additionally makes use of an evolutionary algorithm to allow for a more effective and general optimization. The key idea is to consider a set of potential parameter settings of the breeding program, evaluate their performance based on stochastic simulations, and use these outputs to derive new parameter settings to test in an iterative procedure. The evolutionary algorithm was implemented in a Snakemake workflow management system to allow for efficient scaling on large distributed computing platforms. The algorithm achieved stabilization around the same optimum with a massively reduced number of simulations. Thereby, the incorporation of class variables and accounting for a higher number of parameters in the optimization framework leads to substantially reduced computing time and better scaling for the desired optimization of a breeding program.

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Could evolution be compatible with arguments for design in biology after all?

Kojonen¹

2023

Preprint

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The Design of Evolutionary Algorithms: A Computer Science Perspective on the Compatibility of Evolution and Design

Cited by 3 publications

References 28 publications

On the Relationship between Design and Evolution

On the Relationship between Design and Evolution

Optimization of breeding program design through stochastic simulation with evolutionary algorithms

Could evolution be compatible with arguments for design in biology after all?

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