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Optimized Drug Design using Multi-Objective evolutionary algorithms with SELFIES

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arXiv 2024年

作者： Hömberg, Tomoya Mostaghim, Sanaz Hiwa, Satoru Hiroyasu, Tomoyuki Department of Computational Intelligence Otto-von-Guericke University Magdeburg Germany Faculty of Life and Medical Sciences Doshisha University Kyoto Japan

Computer aided drug design is a promising approach to reduce the tremendous costs, i.e. time and resources, for developing new medicinal drugs. It finds application in aiding the traversal of the vast chemical space of potentially useful compounds. In this paper, we deploy multi-objective evolutionary algorithms, namely NSGA-II, NSGA-III, and MOEA/D, for this purpose. At the same time, we used the SELFIES string representation method. In addition to the QED and SA score, we optimize compounds using the GuacaMol benchmark multi-objective task sets. Our results indicate that all three algorithms show converging behavior and successfully optimize the defined criteria whilst differing mainly in the number of potential solutions found. We observe that novel and promising candidates for synthesis are discovered among obtained compounds in the Pareto-sets. Copyright © 2024, The Authors. All rights reserved.

关键词： evolutionary algorithms

Comparative analysis of various evolutionary algorithms: Past three decades

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第4期11卷

作者： Srikumar, A. Pande, Sagar Dhanraj VIT AP Univ Sch Comp Sci & Engn Amaravati Andhra Pradesh India

INTRODUCTION: The evolutionary algorithms created back in 1953, have gone through various phases of development over the years. It has been put to use to solve various problems in different domains including complex problems such as the infamous problem of Travelling Salesperson (TSP). OBJECTIVES: The main objective of this research is to find out the advancements in evolutionary algorithms and to check whether it is still relevant in 2023. METHODS: To give an overview of the related concepts, subdomains, pros, and cons, the historical and recent developments are discussed and critiqued to provide insights into the results and a better conception of the trends in the domain. RESULTS: For a better perception of the development of evolutionary algorithms over the years, decade-wise trend analysis has been done for the past three decades. CONCLUSION: Scope of research in the domain is ever expanding and to name a few EAs for Data mining, Hybrid EAs are still under development.

关键词： optimization evolutionary algorithms Genetic algorithms Trend Analysis of Genetic algorithms

Performance Comparison of Surrogate-Assisted evolutionary algorithms on Computational Fluid Dynamics Problems

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arXiv 2024年

作者： Kůdela, Jakub Dobrovský, Ladislav Institute of Automation and Computer Science Faculty of Mechanical Engineering Brno University of Technology Czech Republic

Surrogate-assisted evolutionary algorithms (SAEAs) are recently among the most widely studied methods for their capability to solve expensive real-world optimization problems. However, the development of new methods and benchmarking with other techniques still relies almost exclusively on artificially created problems. In this paper, we use two real-world computational fluid dynamics problems to compare the performance of eleven state-of-the-art single-objective SAEAs. We analyze the performance by investigating the quality and robustness of the obtained solutions and the convergence properties of the selected methods. Our findings suggest that the more recently published methods, as well as the techniques that utilize differential evolution as one of their optimization mechanisms, perform significantly better than the other considered methods. © 2024, CC BY.

关键词： evolutionary algorithms

evolutionary algorithms with Self-adjusting Asymmetric Mutation 16th

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Evolutionary Algorithms with Self-adjusting Asymmetric Mutat...

16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Rajabi, Amirhossein Witt, Carsten Tech Univ Denmark Lyngby Denmark

ISBN: (纸本)9783030581114;9783030581121

evolutionary algorithms (EAs) and other randomized search heuristics are often considered as unbiased algorithms that are invariant with respect to different transformations of the underlying search space. However, if a certain amount of domain knowledge is available the use of biased search operators in EAs becomes viable. We consider a simple (1+1) EA for binary search spaces and analyze an asymmetric mutation operator that can treat zero- and one-bits differently. This operator extends previous work by Jansen and Sudholt (ECJ 18(1), 2010) by allowing the operator asymmetry to vary according to the success rate of the algorithm. Using a self-adjusting scheme that learns an appropriate degree of asymmetry, we show improved runtime results on the class of functions OneMaxa describing the number of matching bits with a fixed target alpha is an element of{0, 1}(n).

关键词： evolutionary algorithms Runtime analysis Self-adjusting algorithms Parameter control Asymmetric mutations

Self-Adjusting evolutionary algorithms Are Slow on Multimodal Landscapes

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arXiv 2024年

作者： Lengler, Johannes Sturm, Konstantin Department of Computer Science ETH Zürich Zürich Switzerland

The one-fifth rule and its generalizations are a classical parameter control mechanism in discrete domains. They have also been transferred to control the offspring population size of the (1, λ)-EA. This has been shown to work very well for hill-climbing, and combined with a restart mechanism it was recently shown by Hevia Fajardo and Sudholt to improve performance on the multi-modal problem Cliff drastically. In this work we show that the positive results do not extend to other types of local optima. On the distorted OneMax benchmark, the self-adjusting (1, λ)-EA is slowed down just as elitist algorithms because self-adaptation prevents the algorithm from escaping from local optima. This makes the self-adaptive algorithm considerably worse than good static parameter choices, which do allow to escape from local optima efficiently. We show this theoretically and complement the result with empirical runtime results. © 2024, CC BY.

关键词： evolutionary algorithms

Analysis of Collaborative Multi-Population Based Multi-Objective evolutionary algorithms

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SSRN

SSRN 2024年

作者： Yang, Tianyi Huang, Zhengxin Zhou, Yuren Sun Yat-sen University Guangzhou510275 China Youjiang Medical University for Nationalities Baise533000 China

Multi-population techniques were widely used to improve the optimization performance of nature-inspired optimization algorithms and collaborative multi-population method is a new method in this topic. This paper aims to take a first step towards rigorously analyzing the effectiveness and performance of collaborative multi-population based multi-objective evolutionary algorithms. To be specific, we propose a collaborative multi-population based multi-objective evolutionary algorithm, called *** enhance the performance of the proposed CMPEA, a new indicator for parent selection, called potential degree, is *** present a theoretical analysis of the effectiveness of potential degree and runtime analysis on three optimization problems (including $COCZ$, $COCZ_{TL}$ and $mCOCZ$ benchmark problems). The results show that CMPEA could effectively solve various optimization problems and also has better performance. © 2024, The Authors. All rights reserved.

关键词： evolutionary algorithms

GEEvo: Game Economy Generation and Balancing with evolutionary algorithms

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arXiv 2024年

作者： Rupp, Florian Eckert, Kai Department of Computer Science University of Applied Sciences Mannheim Germany

Game economy design significantly shapes the player experience and progression speed. Modern game economies are becoming increasingly complex and can be very sensitive to even minor numerical adjustments, which may have an unexpected impact on the overall gaming experience. Consequently, thorough manual testing and fine-tuning during development are essential. Unlike existing works that address algorithmic balancing for specific games or genres, this work adopts a more abstract approach, focusing on game balancing through its economy, detached from a specific game. We propose GEEvo (Game Economy Evolution), a framework to generate graph-based game economies and balancing both, newly generated or existing economies. GEEvo uses a two-step approach where evolutionary algorithms are used to first generate an economy and then balance it based on specified objectives, such as generated resources or damage dealt over time. We define different objectives by differently parameterizing the fitness function using data from multiple simulation runs of the economy. To support this, we define a lightweight and flexible game economy simulation framework. Our method is tested and benchmarked with various balancing objectives on a generated dataset, and we conduct a case study evaluating damage balancing for two fictional economies of two popular game character classes. Copyright © 2024, The Authors. All rights reserved.

关键词： evolutionary algorithms

Large Language Models as Surrogate Models in evolutionary algorithms: A Preliminary Study

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arXiv 2024年

作者： Hao, Hao Zhang, Xiaoqun Zhou, Aimin Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China School of Computer Science and Technology East China Normal University Shanghai200062 China Shanghai Institute of AI for Education East China Normal University Shanghai200062 China

Large Language Models (LLMs) have achieved significant progress across various fields and have exhibited strong potential in evolutionary computation, such as generating new solutions and automating algorithm design. Surrogate-assisted selection is a core step in evolutionary algorithms to solve expensive optimization problems by reducing the number of real evaluations. Traditionally, this has relied on conventional machine learning methods, leveraging historical evaluated evaluations to predict the performance of new solutions. In this work, we propose a novel surrogate model based purely on LLM inference capabilities, eliminating the need for training. Specifically, we formulate model-assisted selection as a classification and regression problem, utilizing LLMs to directly evaluate the quality of new solutions based on historical data. This involves predicting whether a solution is good or bad, or approximating its value. This approach is then integrated into evolutionary algorithms, termed LLM-assisted EA (LAEA). Detailed experiments compared the visualization results of 2D data from 9 mainstream LLMs, as well as their performance on optimization problems. The experimental results demonstrate that LLMs have significant potential as surrogate models in evolutionary computation, achieving performance comparable to traditional surrogate models only using inference. This work offers new insights into the application of LLMs in evolutionary computation. Code is available at: https://***/hhyqhh/***. © 2024, CC BY.

关键词： evolutionary algorithms

ARCHIVE-BASED SINGLE-OBJECTIVE evolutionary algorithms FOR SUBMODULAR OPTIMIZATION

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arXiv 2024年

作者： Neumann, Frank Rudolph, Günter School of Computer and Mathematical Sciences The University of Adelaide Australia Department of Computer Science TU Dortmund University Germany

Constrained submodular optimization problems play a key role in the area of combinatorial optimization as they capture many NP-hard optimization problems. So far, Pareto optimization approaches using multi-objective formulations have been shown to be successful to tackle these problems while single-objective formulations lead to difficulties for algorithms such as the (1 + 1)-EA due to the presence of local optima. We introduce for the first time single-objective algorithms that are provably successful for different classes of constrained submodular maximization problems. Our algorithms are variants of the (1 + λ)-EA and (1 + 1)-EA and increase the feasible region of the search space incrementally in order to deal with the considered submodular problems. Copyright © 2024, The Authors. All rights reserved.

关键词： evolutionary algorithms