Papers Evolutionary Algorithms
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Runtime Analysis of a Multi-Valued Compact Genetic Algorithm on Generalized OneMax
A class of metaheuristic techniques called estimation-of-distribution algorithms (EDAs) are employed in optimization as more sophisticated substitutes for traditional strategies like evolutionary algorithms. EDAs general…
Evolutionary AlgorithmsAnalysis of Evolutionary Diversity Optimisation for the Maximum Matching Problem
This paper explores the enhancement of solution diversity in evolutionary algorithms (EAs) for the maximum matching problem, concentrating on complete bipartite graphs and paths. We adopt binary string encoding for match…
DiversityEvolutionary AlgorithmsAdvanced Intelligent Optimization Algorithms for Multi-Objective Optimal Power Flow in Future Power Systems: A Review
This review explores the application of intelligent optimization algorithms to Multi-Objective Optimal Power Flow (MOPF) in enhancing modern power systems. It delves into the challenges posed by the integration of renewa…
Deep Reinforcement LearningEvolutionary AlgorithmsOptimum Beamforming and Grating Lobe Mitigation for Intelligent Reflecting Surfaces
Ensuring adequate wireless coverage in upcoming communication technologies such as 6G is expected to be challenging. This is because user demands of higher datarate require an increase in carrier frequencies, which in tu…
Evolutionary AlgorithmsAnalyzing and Overcoming Local Optima in Complex Multi-Objective Optimization by Decomposition-Based Evolutionary Algorithms
When addressing the challenge of complex multi-objective optimization problems, particularly those with non-convex and non-uniform Pareto fronts, Decomposition-based Multi-Objective Evolutionary Algorithms (MOEADs) often…
DiversityEvolutionary AlgorithmsEvolutionary Preference Sampling for Pareto Set Learning
Recently, Pareto Set Learning (PSL) has been proposed for learning the entire Pareto set using a neural network. PSL employs preference vectors to scalarize multiple objectives, facilitating the learning of mappings from…
Evolutionary AlgorithmsMulti-Objective Evolutionary Algorithms with Sliding Window Selection for the Dynamic Chance-Constrained Knapsack Problem
Evolutionary algorithms are particularly effective for optimisation problems with dynamic and stochastic components. We propose multi-objective evolutionary approaches for the knapsack problem with stochastic profits und…
Evolutionary AlgorithmsNeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks
Evolutionary Algorithms (EAs) play a crucial role in the architectural configuration and training of Artificial Deep Neural Networks (DNNs), a process known as neuroevolution. However, neuroevolution is hindered by its i…
Evolutionary AlgorithmsAchieving Tight $O(4^k)$ Runtime Bounds on Jump$_k$ by Proving that Genetic Algorithms Evolve Near-Maximal Population Diversity
The JUMP$_k$ benchmark was the first problem for which crossover was proven to give a speed-up over mutation-only evolutionary algorithms. Jansen and Wegener (2002) proved an upper bound of $O(\text{poly}(n) + 4^k/p_c)$ …
DiversityEvolutionary AlgorithmsSolving the Food-Energy-Water Nexus Problem via Intelligent Optimization Algorithms
The application of evolutionary algorithms (EAs) to multi-objective optimization problems has been widespread. However, the EA research community has not paid much attention to large-scale multi-objective optimization pr…
Evolutionary AlgorithmsUsing 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem
Real-world optimization problems often involve stochastic and dynamic components. Evolutionary algorithms are particularly effective in these scenarios, as they can easily adapt to uncertain and changing environments but…
Evolutionary AlgorithmsPhylogeny-Informed Interaction Estimation Accelerates Co-Evolutionary Learning
Co-evolution is a powerful problem-solving approach. However, fitness evaluation in co-evolutionary algorithms can be computationally expensive, as the quality of an individual in one population is defined by its interac…
Evolutionary AlgorithmsTemporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation
Many real-world problems have expensive-to-compute fitness functions and are multi-objective in nature. Surrogate-assisted evolutionary algorithms are often used to tackle such problems. Despite this, literature about an…
Evolutionary AlgorithmsEvolving Efficient CNN Based Model for Image Classification
Evolutionary algorithms, rooted in Darwin's theorem, can be considered as a base for implementing deep/machine learning models. This approach can noticeably increase the accuracy in most cases as shown in this paper. Thi…
ClassificationEvolutionary Algorithmsimage-classificationImage Classification+1Minimum variance threshold for epsilon-lexicase selection
Parent selection plays an important role in evolutionary algorithms, and many strategies exist to select the parent pool before breeding the next generation. Methods often rely on average error over the entire dataset as…
Evolutionary AlgorithmsregressionSymbolic RegressionFast Genetic Algorithm for feature selection -- A qualitative approximation approach
Evolutionary Algorithms (EAs) are often challenging to apply in real-world settings since evolutionary computations involve a large number of evaluations of a typically expensive fitness function. For example, an evaluat…
Evolutionary Algorithmsfeature selectionA Flexible Evolutionary Algorithm With Dynamic Mutation Rate Archive
We propose a new, flexible approach for dynamically maintaining successful mutation rates in evolutionary algorithms using $k$-bit flip mutations. The algorithm adds successful mutation rates to an archive of promising r…
Evolutionary AlgorithmsAlready Moderate Population Sizes Provably Yield Strong Robustness to Noise
Experience shows that typical evolutionary algorithms can cope well with stochastic disturbances such as noisy function evaluations. In this first mathematical runtime analysis of the $(1+\lambda)$ and $(1,\lambda)$ evol…
Evolutionary AlgorithmsNeuroLGP-SM: A Surrogate-assisted Neuroevolution Approach using Linear Genetic Programming
Evolutionary algorithms are increasingly recognised as a viable computational approach for the automated optimisation of deep neural networks (DNNs) within artificial intelligence. This method extends to the training of …
Evolutionary AlgorithmsGPUEvolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programming
In this study, we use Genetic Programming (GP) to compose new optimization benchmark functions. Optimization benchmarks have the important role of showing the differences between evolutionary algorithms, making it possib…
Evolutionary Algorithms