Genetic algorithm
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A genetic algorithm (GA) is an optimization technique inspired by biological evolution, using mechanisms such as selection, crossover, and mutation to iteratively evolve a population of candidate solutions toward an optimal outcome. In robotics and AI, genetic algorithms are widely applied to problems that are difficult to solve analytically, including mobile robot path planning, neural network architecture design, controller tuning, and robotic manipulator kinematics. The algorithm encodes potential solutions as chromosomes, evaluates their quality using a fitness function, and repeatedly recombines and mutates high-performing candidates over successive generations until a satisfactory solution emerges. This approach excels in complex, high-dimensional search spaces where traditional methods struggle, such as navigating obstacle-filled environments, balancing robotic assembly lines, or optimizing fuzzy logic controllers. Genetic algorithms matter because they are flexible, domain-agnostic, and capable of escaping local optima, making them a powerful tool for automating design and decision-making processes across a broad range of engineering challenges in autonomous systems and intelligent robotics.
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Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Francisco Herrera, Manuel Lozano, José Luís Verdegay
Citations: 1137 • 1998
A review: On path planning strategies for navigation of mobile robot
B. K. Patle, Ganesh Babu L, Anish Pandey, Dayal R. Parhi, A. Jagadeesh
Citations: 884 • 2019
Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning
Chaymaa Lamini, Said Benhlima, Ali Elbekri
Citations: 466 • 2018
Path Planning for the Mobile Robot: A Review
Weiming Lin, Aixia Chen
Citations: 440 • 2018
Genetic evolution of the topology and weight distribution of neural networks
Vittorio Maniezzo
Citations: 438 • 1994
Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm
Milad Nazarahari, Esmaeel Khanmirza, Samira Doostie
Citations: 435 • 2018
Mobile robot path planning using membrane evolutionary artificial potential field
Ulises Orozco-Rosas, Oscar Montiel, Roberto Sepúlveda
Citations: 412 • 2019
Evolutionary artificial potential fields and their application in real time robot path planning
Prahlad Vadakkepat, Kay Chen Tan, Wang Ming-Liang
Citations: 344 • 2002
Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms
Ricardo Martínez, Oscar Castillo, Luis T. Aguilar
Citations: 339 • 2009
Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles
Oscar Montiel, Ulises Orozco-Rosas, Roberto Sepúlveda
Citations: 336 • 2015
Dynamic path planning of mobile robots with improved genetic algorithm
Adem Tuncer, Mehmet Yıldırım
Citations: 326 • 2012
Automatic Creation of an Autonomous Agent: Genetic Evolution of a Neural Network Driven Robot
Dario Floreano, Francesco Mondada
Citations: 305 • 1994
A knowledge based genetic algorithm for path planning of a mobile robot
Yanrong Hu, Simon X. Yang
Citations: 297 • 2004
Parallel Elite Genetic Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation
Ching‐Chih Tsai, Hsu‐Chih Huang, Cheng-Kai Chan
Citations: 295 • 2011
Automatic Definition of Modular Neural Networks
Frédéric Gruau
Citations: 269 • 1994
Recycling waste classification using optimized convolutional neural network
Wei‐Lung Mao, Wei‐Chun Chen, Chien‐Tsung Wang, Yu‐Hao Lin
Citations: 268 • 2020
Bezier Curve Based Path Planning in a Dynamic Field using Modified Genetic Algorithm
Mohamed Elhoseny, Alaa Tharwat, Aboul Ella Hassanien
Citations: 266 • 2017
Fitness functions in evolutionary robotics: A survey and analysis
Andrew Nelson, Gregory J. Barlow, Lefteris Doitsidis
Citations: 260 • 2008
Evolutionary robots with on-line self-organization and behavioral fitness
Dario Floreano, Joseba Urzelai
Citations: 247 • 2000
Compact Differential Evolution
Ernesto Mininno, Ferrante Neri, Francesco Cupertino, David Naso
Citations: 242 • 2010