24 papers · ranked by Valyu relevance
Saeid Saberi, Alireza Sadat Hosseini, Fatemeh Yazdanifar, Saullo G. P. Castro + 6 more
'Saullo G. P. Castro' 'Cesare Oliviero Rossi' 'Pietro Calandra' 'Paolino Caputo' 'Bagdat Teltayev' 'Valeria Loise' 'Michele Porto'] For the last three decades, bistable composite laminates have gained publicity because of their outstanding features, including having two stable shapes and the ability to change these…
Carlos A. García, Manel Velasco, Cecilio Angulo, Pau Marti + 5 more
'Antonio Camacho' 'Mengchu Zhou' 'Bi Jing' 'Mohammadhossein H. Ghahramani' 'Roberto Teti'] This paper introduces the application of a genetic programming (GP)-based method for the automated design and tuning of process controllers, representing a noteworthy advancement in artificial intelligence (AI) within the realm…
Oladayo S. Ajani, Esther Aboyeji, Rammohan Mallipeddi, Daniel Dooyum Uyeh + 2 more
'Daniel Dooyum Uyeh' 'Yushin Ha' 'Tusan Park'] Optimal sensor location methods are crucial to realize a sensor profile that achieves pre-defined performance criteria as well as minimum cost. In recent times, indoor cultivation systems have leveraged on optimal sensor location schemes for effective monitoring at minimum…
Nicolas Scalzitti, Iliya Miralavy, David E. Korenchan, Christian T. Farrar + 2 more
GP algorithms are powerful evolutionary computing techniques, a branch of AI and are widely used in different fields, such as engineering or bioinformatics . GP is a stochastic algorithm (an extension of GA) inspired by the Darwinian evolution concepts and is useful for automatically solving complex optimization…
Ryan Boldi, Thomas Helmuth, Lee Spector
Down-sampling training data has long been shown to improve the generalization performance of a wide range of machine learning systems. Recently, down-sampling has proved effective in genetic programming (GP) runs that utilize the lexicase parent selection technique. Although this downsampling procedure has been shown…
Sigur de Vries, Sander W. Keemink, Marcel van Gerven
Genetic programming is an optimization algorithm inspired by natural selection which automatically evolves the structure of computer programs. The resulting computer programs are interpretable and efficient compared to black-box models with fixed structure. The fitness evaluation in genetic programming suffers from…
Leonardo Trujillo
Genetic programming (GP) is based on two important insights. First, that any learning task can fundamentally be posed as a program induction problem, where the goal is to construct a symbolic hierarchical model that is expressed as a syntax tree. Second, to pose this task as a search problem, and use evolution to…
Alexander R. Bricco, Iliya Miralavy, Shaowei Bo, Or Perlman + 4 more
Proteins are used by scientists to serve a variety of purposes in clinical practice and laboratory research. To optimize proteins for greater function, a variety of techniques have been developed. For the development of reporter genes used in Magnetic Resonance Imaging (MRI) based on Chemical Exchange Saturation…
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.…
Wolfgang Banzhaf, Illya Bakurov
In this contribution, we discuss the basic concepts of genotypes and phenotypes in tree-based GP (TGP), and then analyze their behavior using five benchmark datasets. We show that TGP exhibits the same behavior that we can observe in other GP representations: At the genotypic level trees show frequently unchecked…
W. B. Langdon
Information theoretic analysis of large evolved programs produced by running genetic programming for up to a million generations has shown even functions as smooth and well behaved as floating point addition and multiplication loose entropy and consequently are robust and fail to propagate disruption to their outputs.…
Pavel Kodytek, Alexandra Bodzas, Jan Zidek, Govind Vashishtha
Continual technological advances associated with the recent automation revolution have tremendously increased the impact of computer technology in the industry. Software development and testing are time-consuming processes, and the current market faces a lack of specialized experts. Introducing automation to this field…
Jarrod Goschen, Anna Sergeevna Bosman, Stefan Grüner
—Ongoing progress in computational intelligence (CI) has led to an increased desire to apply CI techniques for the purpose of improving software engineering processes, particularly software testing. Existing state-of-the-art automated software testing techniques focus on utilising search algorithms to discover input…
Kejia Liu, Yiping Teng, Fang Liu, Ziqiang Zeng
The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. Its restrictions block GEP from successfully handling high-dimensional…
Erik M. Fredericks, Denton Bobeldyk, Jared M. Moore
Outputs in Diverse Fitness Landscapes Authors: ['Erik M. Fredericks' 'Denton Bobeldyk' 'Jared M. Moore'] Abstract Generative art is a rules-driven approach to creating artistic outputs in various mediums. For example, a fluid simulation can govern the flow of colored pixels across a digital display or a rectangle…
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…
Brianna Greenstein, Danielle Elsey, Geoffrey Hutchison
Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse molecular design. However, GAs have multiple hyperparameters that have not been thoroughly investigated for chemical space searches. In this work, we examine the general effects of a number of hyperparameters, such as population…
Alexander Lalejini, Emily Dolson, Anya E. Vostinar, Luis Zaman
Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a…
Jonas Verhellen
Computer-assisted design of small molecules has experienced a resurgence in academic and indus- trial interest due to the widespread use of data-driven techniques such as deep generative models. While the ability to generate molecules that fulfill required chemical properties is encouraging, the use of deep learning…
Authors not listed
More sustainable chemical processes require the selection of suitable molecules, which can be supported by computer-aided molecular design (CAMD). CAMD often generates and evaluates molecular structures using genetic algorithms. However, genetic algorithms can suffer from slow convergence, and might yield suboptimal…
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…
Lillian T. Tatka, Lucian P. Smith, Herbert M. Sauro
Evolutionary algorithms, a class of optimization techniques inspired by biological evolution, have emerged as powerful tools for the optimization of complex systems, including the evolution of mass-action chemical reaction networks. This work explores the application of evolutionary algorithms in this domain…
Kosuke Hamazaki, Hiroyoshi Iwata, Koji Tsuda
Differentiable programming frameworks like PyTorch and JAX revolutionized biological modeling. A foremost merit is that multiple components programmed separately can be put together so that the parameters are jointly optimized. Despite its proven value in agricultural applications, existing breeding simulators are…
Krishna Rijal, Pankaj Mehta
The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the…