17 papers · ranked by Valyu relevance
Urmzd Mukhammadnaim
| AUTHOR: | Urmzd Mukhammadnaim | | |…
Isaac Robledo, Yiqing Li, Guy Y. Cornejo Maceda, Rodrigo Castellanos
The Hybrid Genetic Optimisation framework (HyGO) is introduced to meet the pressing need for efficient and unified optimisation frameworks that support both parametric and functional learning in complex engineering problems. Evolutionary algorithms are widely employed as derivative-free global optimisation methods but…
Mark Kocherovsky, Illya Bakurov, Wolfgang Banzhaf
While crossover is a critical and often indispensable component in other forms of Genetic Programming, such as Linear- and Tree-based, it has consistently been claimed that it deteriorates search performance in CGP. As a result, a mutation-alone (1 + λ ) evolutionary strategy has become the canonical approach for CGP.…
Osval A. Montesinos-López, Abelardo Montesinos-López, Carlos M. Hernández-Suárez, Admas Alemu
Genomic selection (GS) in plant breeding aims to identify individuals with superior genetic merit while maintaining genetic diversity within populations. In plant breeding, considering multiple traits simultaneously makes optimizing selection complex, especially under genetic relatedness constraints. In this study, we…
Tena Škalec, Marko Đurasević, Heming Jia
The container relocation problem (CRP) is a critical optimisation problem in maritime port operations, in which efficient container handling is essential for maximising terminal throughput. Relocation rules (RRs) are a widely adopted solution approach for the CRP, particularly in online and dynamic environments, as…
Duc-Cuong Dang, Roman Kalkreuth, Andre Opris
Cartesian Genetic Programming (CGP) is among the practical and popular forms of Genetic Programming as it uses a graph-based representation of programs. This paper presents a first runtime analysis of CGP in evolving Boolean functions using complete training sets. We prove an asymptotic bound $O(n D^5)$ for the…
Philipp Anthes, Dominik Sobania, Franz Rothlauf
Transformer Semantic Genetic Programming (TSGP) is a semantic search approach that uses a pre-trained transformer model as a variation operator to generate offspring programs with controlled semantic similarity to a given parent. Unlike other semantic GP approaches that rely on fixed syntactic transformations, TSGP…
Duy Long Tran, Anja Jankovic, Marie Anastacio, Holger Hoos + 1 more
Cartesian Genetic Programming has traditionally been using mutation as its main and often sole genetic operator to drive evolutionary search. Despite advancements in recent years, recombinationbased approaches have long been avoided, due to apparent lack of performance gains. This study examines two recently suggested…
Daniel Ortiz-Barrientos, Mark Cooper
Additive models of inheritance predict the short-term response to selection remarkably well, even when the underlying biology involves widespread dominance and gene–gene interaction. We argue that this success reflects a property of how fitness varies with the additive genetic component of a trait, not a property of…
Leonid Chindelevitch, Åsa K Hedman, Dmitri Bichko, Daniel Ziemek + 1 more
Integer linear programming (ILP) is a widely used paradigm in optimization, specifically in situations where certain variables need to take on integer rather than arbitrary real values (). It is a special case of the constraint satisfaction programming paradigm (), which formulates problems as optimization over…
Mauricio González-Forero
Mathematically integrating genetics, development, and evolution is a longstanding challenge. Here I develop general mathematical theory that integrates sexual, discrete, multilocus genetics, development, and evolution. This yields an exact method to describe the evolutionary dynamics of allele frequencies and linkage…
Authors not listed
Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…
Manuel Corpas
Large language model (LLM) agents are typically deployed as clones: identical copies of a single configuration with no mechanism for heritable variation or population-level dynamics. Here we introduce Genomebook, a designed evolutionary system that encodes 26 behavioural traits across 60 diploid loci using additive…
Kengo Sakurai, Laurence Moreau, Tristan Mary-Huard, Alain Charcosset + 1 more
In plant breeding, it is often necessary to improve a target trait while maintaining other essential traits within desirable ranges. When genetic relationships exist among these traits, improvements in the target trait may lead to undesirable changes in essential traits, complicating cross selections. In such cases, it…
Kosuke Hamazaki, Hiroyoshi Iwata, Koji Tsuda, Russell Schwartz
In recent years, machine learning and optimization techniques have transformed numerous fields by providing efficient solutions to complex problems. Similarly, in plant breeding, these techniques have become increasingly important for enhancing breeding strategies through more systematic approaches. Breeding…
Patrik Waldmann, Michael DeGiorgio
SCIP (Solving Constraint Integer Programs) is a powerful and versatile optimization solver and framework that can handle mixed integer linear programs, mixed integer quadratic programs, and general mixed integer non-linear programs with a large range of constraints (). While SCIP can be configured in many ways, its…
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Mesoporous adsorbent materials offer a large volumetric capacity; however, cyclic adsorption/desorption processes in these systems often suffer from hysteresis and may require a significant pressure swing to access this capacity. To mitigate hysteresis, a proposed strategy is to include nucleation sites on the walls of…