19 papers · ranked by Valyu relevance
Wenfen Zhang, Yulin Lan
In many fields, including management, computer, and communication, Large-Scale Global Optimization (LSGO) plays a critical role. It has been applied to various applications and domains. At the same time, it is one of the most challenging optimization problems. This paper proposes a novel memetic algorithm (called MPCE…
Zoran Jakšić, Swagata Devi, Olga Jakšić, Koushik Guha + 3 more
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at…
Maxim Sakharov, Heming Jia
Memetic algorithms achieve strong optimization performance by combining population-based global search with local refinement operators, yet their effectiveness critically depends on the design and management of memes. Local search strategies are typically handcrafted, problem-specific, and fixed prior to execution.…
Raheleh Ghouchan Nezhad Noor Nia, Mehrdad Jalali
Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve…
Somayeh Ranjkesh, Behrooz Masoumi, Seyyed Mohsen Hashemi
Complex networks are used in a variety of applications. Revealing the structure of a community is one of the essential features of a network, during which remote communities are discovered in a complex network. In the real world, dynamic networks are evolving, and the problem of tracking and detecting communities at…
Maoxuan Miao, Jinran Wu, Fengjing Cai, You-Gan Wang + 1 more
'Martino Cassandro'] Simple Summary Due to lacking exploitation capability, traditional genetic algorithm cannot accurately identify the minimal best gene subset. Thus, the improved splicing method is introduced into a genetic algorithm to enhance exploitation capability for achieving balance between exploitation and…
Utku Umur Acikalin, Bugra Caskurlu
The Hypergraph Partitioning (HGP) problem is a well-studied problem that finds applications in a variety of domains. The literature on the HGP problem has heavily focused on developing fast heuristic approaches. In several application domains, such as the VLSI design and database migration planning, the quality of the…
Szilárd Kovács, Csaba Budai, János Botzheim
In this paper, we present the Colonial Bacterial Memetic Algorithm (CBMA), an advanced evolutionary optimization approach for robotic applications. CBMA extends the Bacterial Memetic Algorithm by integrating Cultural Algorithms and co-evolutionary dynamics inspired by bacterial group behavior. This combination of…
Zhiwei Zhang, Jiayu Shen, Niraj Kumar, Marco Pistoia
Low Autocorrelation Binary Sequences (LABS) is a particularly challenging binary optimization problem which quickly becomes intractable in finding the global optimum for problem sizes beyond 66. This aspect makes LABS appealing to use as a test-bed for meta-heuristic optimization solvers to target large problem sizes.…
Manon Flageat, Bryan Lim, Antoine Cully
With the development of hardware accelerators and their corresponding tools, evaluations have become more affordable through fast and massively parallel evaluations in some applications. This advancement has drastically sped up the runtime of evolutioninspired algorithms such as Quality-Diversity (QD) optimization…
F. Pigozzi, T. Cirrito, M. Levin
Molecular pathways such as gene-regulatory networks regulate numerous functions in cells and tissues that impact embryonic development, regenerative repair, aging, cancer, and many other aspects of health and disease. One important aspect of such networks is experience-dependent plasticity: their activity changes after…
Prasad U. Bandodkar, Razeen R. Shaikh, Gregory T. Reeves
Model development is essential to gain a mathematical understanding of the underlying phenomena in systems biology. In most models, it is typically hard to estimate the values of the biophysical/phenomenological parameters that characterize the model. The parameters are estimated by minimizing a function that reduces 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…
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…
Nam Hai Le
The gene-centric paradigm, formalized in the Modern Synthesis and operationalized through genetic algorithms, attributes evolutionary causality exclusively to genes; organisms are passive vehicles for genetic replication. Denis Noble's phenotype-first framework challenges this view, arguing that organisms are active…
Kathleen S. Dreyer, Anh V. Nguyen, Gauri G. Bora, Lauren E. Redus + 6 more
Genetic programs can direct living systems to perform diverse, pre-specified functions. As the library of parts available for building such programs continues to expand, computation-guided design is increasingly helpful and necessary. Predictive models aid the challenging design process, but iterative simulation and…
Ulises Sánchez Iñiguez, Sara Lledó Villaescusa, Rafael Lahoz-Beltra
Synthetic biology applications are currently based on the programming of bacteria with tailor-made circuits designed ad hoc by applying a top-down strategy. We introduce a novel algorithm oriented to design synthetic bacteria according to a bottom-up approach, i.e. via an ‘evolutionary programming’ algorithm. The…
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…
Authors not listed
Genetic Algorithms are a powerful method to solve optimization problems with complex cost functions over vast search spaces that rely in particular on recombining parts of previous solutions. Crossover operators play a crucial role in this context. Here, we describe a large class of these operators designed for…