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Found 31 papers, 11 papers with code
Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution
Many real-world control and classification tasks involve a large number of features.
Compact Optimization Algorithms with Re-sampled Inheritance
The resulting compact algorithms with RI are tested on the CEC 2014 benchmark functions.
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.
Multi-Strategy Coevolving Aging Particle Optimization
We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization.
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.
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.
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.
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.
Novelty Producing Synaptic Plasticity
A learning process with the plasticity property often requires reinforcement signals to guide the process.
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.
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.
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.
Ranked #1 on
OpenAI Gym
on Cart Pole (OpenAI Gym)
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.
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).
A Signal-Centric Perspective on the Evolution of Symbolic Communication
The evolution of symbolic communication is a longstanding open research question in biology.
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.
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.
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.
Interpretable pipelines with evolutionarily optimized modules for RL tasks with visual inputs
However, they struggle when working with raw data, especially when the input dimensionality increases and the raw inputs alone do not give valuable insights on the decision-making process.
Interpretable AI for policy-making in pandemics
For this reason, several works have applied machine learning techniques, often with the help of special-purpose simulators, to generate policies that were more effective than the ones obtained by governments.
Large-scale multi-objective influence maximisation with network downscaling
Finding the most influential nodes in a network is a computationally hard problem with several possible applications in various kinds of network-based problems.
Online Distributed Evolutionary Optimization of Time Division Multiple Access Protocols
With the advent of cheap, miniaturized electronics, ubiquitous networking has reached an unprecedented level of complexity, scale and heterogeneity, becoming the core of several modern applications such as smart industry, smart buildings and smart cities.
Reinforcement learning based adaptive metaheuristics
We demonstrate the applicability of this framework on two algorithms, namely Covariance Matrix Adaptation Evolution Strategies (CMA-ES) and Differential Evolution (DE), for which we learn, respectively, adaptation policies for the step-size (for CMA-ES), and the scale factor and crossover rate (for DE).
The emergence of division of labor through decentralized social sanctioning
Here we show that by introducing a model of social norms, which we regard as emergent patterns of decentralized social sanctioning, it becomes possible for groups of self-interested individuals to learn a productive division of labor involving all critical roles.
Quality Diversity Evolutionary Learning of Decision Trees
Addressing the need for explainable Machine Learning has emerged as one of the most important research directions in modern Artificial Intelligence (AI).
Self-building Neural Networks
We compare our proposed SBNN with traditional neural networks (NNs) over three classical control tasks from OpenAI.
Peeking inside Sparse Neural Networks using Multi-Partite Graph Representations
Modern Deep Neural Networks (DNNs) have achieved very high performance at the expense of computational resources.
Social Interpretable Reinforcement Learning
Our method mimics a social learning process, where each agent in a group learns to solve a given task based both on its own individual experience as well as the experience acquired together with its peers.
Neuron-centric Hebbian Learning
To overcome this limitation, we propose a novel plasticity model, called Neuron-centric Hebbian Learning (NcHL), where optimization focuses on neuron- rather than synaptic-specific Hebbian parameters.
Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time
The Influence Maximization (IM) problem seeks to discover the set of nodes in a graph that can spread the information propagation at most.
MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning
In this challenging setting, the transferable representations already encoded in the pretrained model are a key aspect to preserve.
