Papers Evolutionary Algorithms
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Evolutionary algorithms as an alternative to backpropagation for supervised training of Biophysical Neural Networks and Neural ODEs
Training networks consisting of biophysically accurate neuron models could allow for new insights into how brain circuits can organize and solve tasks. We begin by analyzing the extent to which the central algorithm for …
Evolutionary AlgorithmsHardest Monotone Functions for Evolutionary Algorithms
The study of hardest and easiest fitness landscapes is an active area of research. Recently, Kaufmann, Larcher, Lengler and Zou conjectured that for the self-adjusting $(1,\lambda)$-EA, Adversarial Dynamic BinVal (ADBV) …
Evolutionary AlgorithmsBenchmarking Differential Evolution on a Quantum Simulator
The use of Evolutionary Algorithms (EA) for solving Mathematical/Computational Optimization Problems is inspired by the biological processes of Evolution. Few of the primitives involved in the Evolutionary process/paradi…
BenchmarkingEvolutionary AlgorithmsEvolutionary Tabletop Game Design: A Case Study in the Risk Game
Creating and evaluating games manually is an arduous and laborious task. Procedural content generation can aid by creating game artifacts, but usually not an entire game. Evolutionary game design, which combines evolutio…
Board GamesEvolutionary AlgorithmsGame DesignEver Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation
We introduce EV3, a novel meta-optimization framework designed to efficiently train scalable machine learning models through an intuitive explore-assess-adapt protocol. In each iteration of EV3, we explore various model …
DiversityEvolutionary AlgorithmsKnowledge DistillationMeta-Learning+1Large Language Models as Evolutionary Optimizers
Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex combinatorial optimization problems. However, EAs often demand carefully-designed operators with the aid of domain expertise to achieve s…
Combinatorial OptimizationEvolutionary AlgorithmsEfficient CPU-Optimized Parameter Estimation for Modeling Fish Schooling Behavior in Large Particle Systems
The schooling behavior of fish can be studied through simulations involving a large number of interacting particles. In such systems, each individual particle is guided by behavior rules, which include aggregation toward…
Collision AvoidanceCPUEvolutionary Algorithmsparameter estimationLarge Language Model for Multi-objective Evolutionary Optimization
Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of which the search operators need a carefully …
Evolutionary AlgorithmsLanguage ModelingLanguage ModellingLarge Language Model+3Performance Evaluation of Evolutionary Algorithms for Analog Integrated Circuit Design Optimisation
An automated sizing approach for analog circuits using evolutionary algorithms is presented in this paper. A targeted search of the search space has been implemented using a particle generation function and a repair-boun…
Evolutionary AlgorithmsSolution to Advanced Manufacturing Process Problems using Cohort Intelligence Algorithm with Improved Constraint Handling Approaches
Recently, various Artificial Intelligence (AI) based optimization metaheuristics are proposed and applied for a variety of problems. Cohort Intelligence (CI) algorithm is a socio inspired optimization technique which is …
Evolutionary AlgorithmsTowards Running Time Analysis of Interactive Multi-objective Evolutionary Algorithms
Evolutionary algorithms (EAs) are widely used for multi-objective optimization due to their population-based nature. Traditional multi-objective EAs (MOEAs) generate a large set of solutions to approximate the Pareto fro…
Decision MakingEvolutionary AlgorithmsEvolutionary Dynamic Optimization and Machine Learning
Evolutionary Computation (EC) has emerged as a powerful field of Artificial Intelligence, inspired by nature's mechanisms of gradual development. However, EC approaches often face challenges such as stagnation, diversity…
DiversityEvolutionary Algorithmsfeature selectionAbCD: A Component-wise Adjustable Framework for Dynamic Optimization Problems
Dynamic Optimization Problems (DOPs) are characterized by changes in the fitness landscape that can occur at any time and are common in real world applications. The main issues to be considered include detecting the chan…
Evolutionary AlgorithmsBivariate Estimation-of-Distribution Algorithms Can Find an Exponential Number of Optima
Finding a large set of optima in a multimodal optimization landscape is a challenging task. Classical population-based evolutionary algorithms typically converge only to a single solution. While this can be counteracted …
Evolutionary AlgorithmsDesign Optimizer for Planar Soft-Growing Robot Manipulators
Soft-growing robots are innovative devices that feature plant-inspired growth to navigate environments. Thanks to their embodied intelligence of adapting to their surroundings and the latest innovation in actuation and m…
Evolutionary AlgorithmsNavigateAn automatic selection of optimal recurrent neural network architecture for processes dynamics modelling purposes
A problem related to the development of algorithms designed to find the structure of artificial neural network used for behavioural (black-box) modelling of selected dynamic processes has been addressed in this paper. Th…
Evolutionary AlgorithmsReinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressions
Feature transformation aims to generate new pattern-discriminative feature space from original features to improve downstream machine learning (ML) task performances. However, the discrete search space for the optimal fe…
Evolutionary AlgorithmsAxOMaP: Designing FPGA-based Approximate Arithmetic Operators using Mathematical Programming
With the increasing application of machine learning (ML) algorithms in embedded systems, there is a rising necessity to design low-cost computer arithmetic for these resource-constrained systems. As a result, emerging mo…
Evolutionary AlgorithmsSpeeding-up Evolutionary Algorithms to solve Black-Box Optimization Problems
Population-based evolutionary algorithms are often considered when approaching computationally expensive black-box optimization problems. They employ a selection mechanism to choose the best solutions from a given popula…
Evolutionary AlgorithmsEnhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization
Surrogate-assisted evolutionary algorithms (SAEAs) hold significant importance in resolving expensive optimization problems~(EOPs). Extensive efforts have been devoted to improving the efficacy of SAEAs through the devel…
Evolutionary AlgorithmsRelation