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Found 18 papers, 6 papers with code
Graph-Aware Evolutionary Algorithms for Influence Maximization
Social networks represent nowadays in many contexts the main source of information transmission and the way opinions and actions are influenced.
Black-box adversarial attacks using Evolution Strategies
Several methods able to generate adversarial samples make use of gradients, which usually are not available to an attacker in real-world scenarios.
Seeking Quality Diversity in Evolutionary Co-design of Morphology and Control of Soft Tensegrity Modular Robots
Designing optimal soft modular robots is difficult, due to non-trivial interactions between morphology and controller.
A Signal-Centric Perspective on the Evolution of Symbolic Communication
The evolution of symbolic communication is a longstanding open research question in biology.
A Framework for Knowledge Integrated Evolutionary Algorithms
Notably, the KIEA framework is EA-agnostic (i. e., it works with any evolutionary algorithm), problem-independent (i. e., it is not dedicated to a specific type of problems), expandable (i. e., its knowledge base can grow over time).
Genetic Improvement of Routing Protocols for Delay Tolerant Networks
We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes, and find that GI produces consistent gains in delivery probability in four cases.
Evolutionary learning of interpretable decision trees
We present a two-level optimization scheme that combines the advantages of evolutionary algorithms with the advantages of Q-learning.
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EVO-RL: Evolutionary-Driven Reinforcement Learning
Furthermore, we propose that this distinction is decided by the evolutionary process, thus allowing evo-RL to be adaptive to different environments.
Distributed Embodied Evolution over Networks
In several network problems the optimum behavior of the agents (i. e., the nodes of the network) is not known before deployment.
Novelty Producing Synaptic Plasticity
A learning process with the plasticity property often requires reinforcement signals to guide the process.
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search
Although very effective, evolutionary algorithms rely heavily on having a large population of individuals (i. e., network architectures) and is therefore memory expensive.
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions
Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons.
Learning with Delayed Synaptic Plasticity
Inspired by biology, plasticity can be modeled in artificial neural networks by using Hebbian learning rules, i. e. rules that update synapses based on the neuron activations and reinforcement signals.
Evaluating MAP-Elites on Constrained Optimization Problems
As such, it could be used in the future as an effective building-block for designing new constrained optimization algorithms.
Multi-Strategy Coevolving Aging Particle Optimization
We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization.
Distributed optimization in wireless sensor networks: an island-model framework
We perform extensive tests of different DOWSN configurations on a benchmark made up of continuous optimization problems; we analyze the influence of the network parameters (number of nodes, inter-node communication period and probability of accepting incoming solutions) on the optimization performance.
Compact Optimization Algorithms with Re-sampled Inheritance
The resulting compact algorithms with RI are tested on the CEC 2014 benchmark functions.
Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution
Many real-world control and classification tasks involve a large number of features.