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08/08/2023
Asynchronous Evolution of Deep Neural Network Architectures
Many evolutionary algorithms (EAs) take advantage of parallel evaluation...
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06/18/2023
Evolving Strategies for Competitive Multi-Agent Search
While evolutionary computation is well suited for automatic discovery in...
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02/13/2023
Accelerating Evolution Through Gene Masking and Distributed Search
In building practical applications of evolutionary computation (EC), two...
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01/13/2023
Efficient Activation Function Optimization through Surrogate Modeling
Carefully designed activation functions can improve the performance of n...
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10/25/2022
Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture Search
Evolutionary algorithms (EAs) have gained attention recently due to thei...
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04/21/2022
Evolution of Transparent Explainable Rule-sets
Most AI systems are black boxes generating reasonable outputs for given ...
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04/11/2022
Effective Mutation Rate Adaptation through Group Elite Selection
Evolutionary algorithms are sensitive to the mutation rate (MR); no sing...
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03/14/2022
DIAS: A Domain-Independent Alife-Based Problem-Solving System
A domain-independent problem-solving system based on principles of Artif...
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02/19/2022
Simple Genetic Operators are Universal Approximators of Probability Distributions (and other Advantages of Expressive Encodings)
This paper characterizes the inherent power of evolutionary algorithms. ...
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10/26/2021
NeuroComb: Improving SAT Solving with Graph Neural Networks
Propositional satisfiability (SAT) is an NP-complete problem that impact...
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09/18/2021
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks
Neural networks require careful weight initialization to prevent signals...
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02/17/2021
Evolving GAN Formulations for Higher Quality Image Synthesis
Generative Adversarial Networks (GANs) have extended deep learning to co...
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10/05/2020
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings
This paper frames a general prediction system as an observer traveling a...
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10/05/2020
Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model
As neural network classifiers are deployed in real-world applications, i...
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10/02/2020
Effective Regularization Through Loss-Function Metalearning
Loss-function metalearning can be used to discover novel, customized los...
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08/04/2020
Creative AI Through Evolutionary Computation: Principles and Examples
The main power of artificial intelligence is not in modeling what we alr...
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06/18/2020
Generalization of Agent Behavior through Explicit Representation of Context
In order to deploy autonomous agents in digital interactive environments...
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06/05/2020
Discovering Parametric Activation Functions
Recent studies have shown that the choice of activation function can sig...
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05/28/2020
From Prediction to Prescription: AI-Based Optimization of Non-Pharmaceutical Interventions for the COVID-19 Pandemic
Several models have been developed to predict how the COVID-19 pandemic ...
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02/17/2020
Evolutionary Optimization of Deep Learning Activation Functions
The choice of activation function can have a large effect on the perform...
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02/13/2020
Adapting to Unseen Environments through Explicit Representation of Context
In order to deploy autonomous agents to domains such as autonomous drivi...
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02/13/2020
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription
There is now significant historical data available on decision making in...
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02/11/2020
Population-Based Training for Loss Function Optimization
Metalearning of deep neural network (DNN) architectures and hyperparamet...
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02/09/2020
Improving Neural Network Learning Through Dual Variable Learning Rates
This paper introduces and evaluates a novel training method for neural n...
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02/09/2020
MDEA: Malware Detection with Evolutionary Adversarial Learning
Malware detection have used machine learning to detect malware in progra...
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01/31/2020
Evolving Loss Functions With Multivariate Taylor Polynomial Parameterizations
Loss function optimization for neural networks has recently emerged as a...
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06/07/2019
Enhanced Optimization with Composite Objectives and Novelty Pulsation
An important benefit of multi-objective search is that it maintains a di...
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06/03/2019
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel
Neural Networks (NNs) have been extensively used for a wide spectrum of ...
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05/31/2019
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
As deep learning applications continue to become more diverse, an intere...
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05/30/2019
Better Future through AI: Avoiding Pitfalls and Guiding AI Towards its Full Potential
Artificial Intelligence (AI) technology is rapidly changing many areas o...
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05/27/2019
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization
As the complexity of neural network models has grown, it has become incr...
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03/25/2019
Functional Generative Design of Mechanisms with Recurrent Neural Networks and Novelty Search
Consumer-grade 3D printers have made it easier to fabricate aesthetic ob...
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02/18/2019
Evolutionary Neural AutoML for Deep Learning
Deep neural networks (DNNs) have produced state-of-the-art results in ma...
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01/12/2019
Creative AI Through Evolutionary Computation
In the last decade or so we have seen tremendous progress in Artificial ...
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11/30/2018
Hierarchical Policy Design for Sample-Efficient Learning of Robot Table Tennis Through Self-Play
Training robots with physical bodies requires developing new methods and...
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08/25/2018
A Comparison of the Taguchi Method and Evolutionary Optimization in Multivariate Testing
Multivariate testing has recently emerged as a promising technique in we...
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04/19/2018
Functional Generative Design: An Evolutionary Approach to 3D-Printing
Consumer-grade printers are widely available, but their ability to print...
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03/12/2018
From Nodes to Networks: Evolving Recurrent Neural Networks
Gated recurrent networks such as those composed of Long Short-Term Memor...
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03/11/2018
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back
Deep multitask learning boosts performance by sharing learned structure ...
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03/10/2018
Evolutionary Architecture Search For Deep Multitask Networks
Multitask learning, i.e. learning several tasks at once with the same ne...
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03/10/2018
Enhanced Optimization with Composite Objectives and Novelty Selection
An important benefit of multi-objective search is that it maintains a di...
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03/10/2018
Enhancing Evolutionary Optimization in Uncertain Environments by Allocating Evaluations via Multi-armed Bandit Algorithms
Optimization problems with uncertain fitness functions are common in the...
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03/09/2018
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Biological evolution provides a creative fount of complex and subtle ada...
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10/31/2017
Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering
Existing deep multitask learning (MTL) approaches align layers shared be...
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04/18/2017
Discovering Evolutionary Stepping Stones through Behavior Domination
Behavior domination is proposed as a tool for understanding and harnessi...
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03/01/2017
Conversion Rate Optimization through Evolutionary Computation
Conversion optimization means designing a web interface so that as many ...
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03/01/2017
Evolving Deep Neural Networks
The success of deep learning depends on finding an architecture to fit t...
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12/04/2015
Reuse of Neural Modules for General Video Game Playing
A general approach to knowledge transfer is introduced in which an agent...
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10/27/2015