Tomasz Tarkowski
This work discusses single-objective constrained genetic algorithm with floating-point, integer, binary and permutation representation. Floating-point genetic algorithm tuning with use of test functions is done and leads to a parameterization with comparatively outstanding performance.
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Finding the best configuration of algorithms' hyperparameters for a given optimization problem is an important task in evolutionary computation. We compare in this work the results of four different hyperparameter tuning approaches for a family of genetic algorithms on 25 diverse pseudo-Boolean optimization problems. More precisely, we compare previously obtained results from a grid search with those obtained from three automated configuration techniques: iterated racing, mixed-integer parallel efficient global optimization, and mixed-integer evolutionary strategies. Using two different cost m…
Genetic algorithms are a well-known method for tackling the problem of variable selection. As they are non-parametric and can use a large variety of fitness functions, they are well-suited as a variable selection wrapper that can be applied to many different models. In almost all cases, the chromosome formulation used in these genetic algorithms consists of a binary vector of length n for n potential variables indicating the presence or absence of the corresponding variables. While the aforementioned chromosome formulation has exhibited good performance for relatively small n, there are potent…
A Linear Programming Enhanced Genetic Algorithm for Hyperparameter Tuning in Machine Learning
本文将超参数调优建模为双层规划，提出用线性规划增强微型遗传算法，在离散与连续超参数上协同搜索，并在MNIST和CIFAR-10上验证了其与种群方法结合的有效性。
With neural networks having demonstrated their versatility and benefits, the need for their optimal performance is as prevalent as ever. A defining characteristic, hyperparameters, can greatly affect its performance. Thus engineers go through a process, tuning, to identify and implement optimal hyperparameters. That being said, excess amounts of manual effort are required for tuning network architectures, training configurations, and preprocessing settings such as Byte Pair Encoding (BPE). In this study, we propose an automatic tuning method modeled after Darwin's Survival of the Fittest Theor…
GEGO: A Hybrid Golden Eagle and Genetic Optimization Algorithm for Efficient Hyperparameter Tuning in Resource-Constrained Environments
GEGO通过融合黄金鹰优化与遗传算法，实现了在资源受限环境下高效超参数调优，提升了搜索的多样性和稳定性。
Hybrid Genetic Algorithm and Mixed Integer Linear Programming for Flying Sidekick TSP
针对卡车与无人机协同的 FSTSP，本文提出 MILP 数学建模及融合专用启发式与局部搜索的 HGenFS，精确法可解十客户规模，HGenFS 数秒内求得最优解。
Superior Genetic Algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy Heuristics
针对目标集选择问题，提出通过幂律分布随机选择参数并添加丢弃非必要小度顶点的贪心启发式，改进BRKGA算法，使其在无参数调优下优于现有最先进算法。
This paper presents the Anisotropic selection scheme for cellular Genetic Algorithms (cGA). This new scheme allows to enhance diversity and to control the selective pressure which are two important issues in Genetic Algorithms, especially when trying to solve difficult optimization problems. Varying the anisotropic degree of selection allows swapping from a cellular to an island model of parallel genetic algorithm. Measures of performances and diversity have been performed on one well-known problem: the Quadratic Assignment Problem which is known to be difficult to optimize. Experiences show t…
Improved Model Reduction and Tuning of Fractional Order PIλDμ Controllers for Analytical Rule Extraction with Genetic Programming
本文提出一种基于遗传算法在奈奎斯特平面上的次优模型降阶新方法，并结合时域最优调谐，通过遗传编程从高阶过程数据中提取PID和分数阶PIλDμ控制器的解析调谐规则，实现了性能与复杂度的权衡。
Recently, program autotuning has become very popular especially in embedded systems, when we have limited resources such as computing power and memory where these systems run generally time-critical applications. Compiler optimization space gradually expands with the renewed compiler options and inclusion of new architectures. These advancements bring autotuning even more important position. In this paper, we introduced Flag Optimization with Genetic Algorithm (FOGA) as an autotuning solution for GCC flag optimization. FOGA has two main advantages over the other autotuning approaches: the firs…
Convolutional Neural Networks (CNNs) have gained a remarkable success on many image classification tasks in recent years. However, the performance of CNNs highly relies upon their architectures. For most state-of-the-art CNNs, their architectures are often manually-designed with expertise in both CNNs and the investigated problems. Therefore, it is difficult for users, who have no extended expertise in CNNs, to design optimal CNN architectures for their own image classification problems of interest. In this paper, we propose an automatic CNN architecture design method by using genetic algorith…
Most real-world optimization problems are difficult to solve with traditional statistical techniques or with metaheuristics. The main difficulty is related to the existence of a considerable number of local optima, which may result in the premature convergence of the optimization process. To address this problem, we propose a novel heuristic method for constructing a smooth surrogate model of the original function. The surrogate function is easier to optimize but maintains a fundamental property of the original rugged fitness landscape: the location of the global optimum. To create such a surr…
Biased Random-Key Genetic Algorithms: A Review
本文综述了有偏随机键遗传算法（BRKGA），涵盖其基于随机键的染色体与有偏、均匀及精英交配策略，总结了150余篇论文的应用，指出调度为主要领域，并展望了未来研究方向。
Convolutional Neural Networks (CNNs) have gained a significant attraction in the recent years due to their increasing real-world applications. Their performance is highly dependent to the network structure and the selected optimization method for tuning the network parameters. In this paper, we propose novel yet efficient methods for training convolutional neural networks. The most of current state of the art learning method for CNNs are based on Gradient decent. In contrary to the traditional CNN training methods, we propose to optimize the CNNs using methods based on Genetic Algorithms (GAs)…
Tuning Synaptic Connections instead of Weights by Genetic Algorithm in Spiking Policy Network
本文提出用遗传算法优化模仿昆虫感觉运动通路的脉冲策略网络，通过调节突触连接而非权重，在机器人控制任务中达到主流深度强化学习同等性能且能效显著更高。
A genetic algorithm for student academic resource allocation
研究高中数学学生学术资源分配的0-1二元组合优化问题，提出集成专门约束修复机制的遗传算法，经实验评估，该算法能快速收敛、求解质量高且稳定性强，证实元启发式方法对中等教育实时决策支持系统有用。
The global market for textile industry is highly competitive nowadays. Quality control in production process in textile industry has been a key factor for retaining existence in such competitive market. Automated textile inspection systems are very useful in this respect, because manual inspection is time consuming and not accurate enough. Hence, automated textile inspection systems have been drawing plenty of attention of the researchers of different countries in order to replace manual inspection. Defect detection and defect classification are the two major problems that are posed by the res…
Early years of Biased Random-Key Genetic Algorithms: A systematic review
本文系统综述了偏置随机键遗传算法（BRKGA）的早期发展，涵盖约250篇论文，分析其方法、应用及未来研究方向。
Traditional Genetic Algorithms (GAs) mating schemes select individuals for crossover independently of their genotypic or phenotypic similarities. In Nature, this behaviour is known as random mating. However, non-random schemes - in which individuals mate according to their kinship or likeness - are more common in natural systems. Previous studies indicate that, when applied to GAs, negative assortative mating (a specific type of non-random mating, also known as dissortative mating) may improve their performance (on both speed and reliability) in a wide range of problems. Dissortative mating ma…
Traditionally Genetic Algorithm has been used for optimization of unimodal and multimodal functions. Earlier researchers worked with constant probabilities of GA control operators like crossover, mutation etc. for tuning the optimization in specific domains. Recent advancements in this field witnessed adaptive approach in probability determination. In Adaptive mutation primarily poor individuals are utilized to explore state space, so mutation probability is usually generated proportionally to the difference between fitness of best chromosome and itself (fMAX - f). However, this approach is su…
A genetic algorithm (GA) is a search method that optimises a population of solutions by simulating natural evolution. Good solutions reproduce together to create better candidates. The standard GA assumes that any two solutions can mate. However, in nature and social contexts, social networks can condition the likelihood that two individuals mate. This impact of population network structure over GAs performance is unknown. Here we introduce the Networked Genetic Algorithm (NGA) to evaluate how various random and scale-free population networks influence the optimisation performance of GAs on be…
A Genetic Algorithm-Based Approach for Automated Optimization of Kolmogorov-Arnold Networks in Classification Tasks
本文提出GA-KAN，一种基于遗传算法的KANs自动化优化方法，通过引入稀疏连接和新的编解码策略，在多个分类数据集上提升了模型准确性与可解释性，同时显著减少了参数数量。
Constraint-aware Optimization in Auto-Tuning
提出四种约束感知进化算法用于自动调优，通过实验证明其相比无约束方法收敛更快、性能更优，平均效率提升约39%，并作为开源贡献集成到Kernel Tuner框架。
Accelerating Evolution: Integrating PSO Principles into Real-Coded Genetic Algorithm Crossover
本文提出融合粒子群优化思想的实数编码遗传算法交叉算子PSOX，利用全局最优与历史最优信息加速收敛并保持多样性，在15个基准函数上验证其优于五种先进交叉算子，并给出变异率调参指导。
Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in commercial and industrial environment. In this paper we present an interesting application of the fuzzy-GA paradigm to Smart Grids. The main aim consists in performing decision making for power flow management tasks in the proposed microgrid model equipped by renewable sources and an energy storage system, taking into account the economical profit in energy trading with the main-grid. In particular, this study focuses on the application of a Hierarchical Genetic…
This paper will propose a novel technique for optimize hydropower plant in small scale based on load frequency control (LFC) which use self-tuning fuzzy Proportional- Derivative (PD) method for estimation and prediction of planning. Due to frequency is not controlled by any dump load or something else, so this power plant is under dynamic frequency variations that will use PD controller which optimize by fuzzy rules and then with neural deep learning techniques and Genetic Algorithm optimization. The main purpose of this work is because to maintain frequency in small-hydropower plant at nomina…
In recent years, people from all over the world are suffering from one of the most severe diseases in history, known as Coronavirus disease 2019, COVID-19 for short. When the virus reaches the lungs, it has a higher probability to cause lung pneumonia and sepsis. X-ray image is a powerful tool in identifying the typical features of the infection for COVID-19 patients. The radiologists and pathologists observe that ground-glass opacity appears in the chest X-ray for infected patient \cite{cozzi2021ground}, and it could be used as one of the criteria during the diagnosis process. In the past few…
Pascal-Weighted Genetic Algorithms: A Binomially-Structured Recombination Framework
本文提出了一种基于帕斯卡权重的多亲本重组框架，通过二项式权重结构实现更稳定的遗传算法性能，显著提升收敛效率和稳定性。
This paper characterizes and discusses devolutionary genetic algorithms and evaluates their performances in solving the minimum labeling Steiner tree (MLST) problem. We define devolutionary algorithms as the process of reaching a feasible solution by devolving a population of super-optimal unfeasible solutions over time. We claim that distinguishing them from the widely used evolutionary algorithms is relevant. The most important distinction lies in the fact that in the former type of processes, the value function decreases over successive generation of solutions, thus providing a natural stop…
Dual-Individual Genetic Algorithm: A Dual-Individual Approach for Efficient Training of Multi-Layer Neural Networks
提出仅含 Leader 与 Follower 两个体的增强遗传算法，通过自适应层维度和 Pareto 排序优化二值图像分类网络，以更低成本取得高于梯度方法的训练准确率。
Towards Self-Evolving Agents: A Human-Inspired Adaptive Exploration-Exploitation Framework for Genetic Network Programming
该研究受人类发育模式启发，提出人类启发的遗传网络编程（HGNP）框架，通过新型自适应交叉、变异算子及循环消除机制，动态调节遗传网络编程中探索与利用的平衡，提升了智能体策略性能，且可应用于多种GNP变体。
In this paper we introduce a novel method for automatically tuning the search parameters of a chess program using genetic algorithms. Our results show that a large set of parameter values can be learned automatically, such that the resulting performance is comparable with that of manually tuned parameters of top tournament-playing chess programs.
We propose a general formulation of a univariate estimation-of-distribution algorithm (EDA). It naturally incorporates the three classic univariate EDAs \emph{compact genetic algorithm}, \emph{univariate marginal distribution algorithm} and \emph{population-based incremental learning} as well as the \emph{max-min ant system} with iteration-best update. Our unified description of the existing algorithms allows a unified analysis of these; we demonstrate this by providing an analysis of genetic drift that immediately gives the existing results proven separately for the four algorithms named abov…
In this paper, we introduce a Model-based Algorithm Turning Engine, namely MATE, where the parameters of an algorithm are represented as expressions of the features of a target optimisation problem. In contrast to most static (feature-independent) algorithm tuning engines such as irace and SPOT, our approach aims to derive the best parameter configuration of a given algorithm for a specific problem, exploiting the relationships between the algorithm parameters and the features of the problem. We formulate the problem of finding the relationships between the parameters and the problem features …
A novel evolutionary approach for Explainable Artificial Intelligence is presented: the "Evolved Explanations" model (EvEx). This methodology consists in combining Local Interpretable Model Agnostic Explanations (LIME) with Multi-Objective Genetic Algorithms to allow for automated segmentation parameter tuning in image classification tasks. In this case, the dataset studied is Patch-Camelyon, comprised of patches from pathology whole slide images. A publicly available Convolutional Neural Network (CNN) was trained on this dataset to provide a binary classification for presence/absence of lymph…
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