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GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference
ACM2019 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
GECCO '19: Genetic and Evolutionary Computation Conference Prague Czech Republic July 13 - 17, 2019
ISBN:
978-1-4503-6111-8
Published:
13 July 2019
Sponsors:
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Abstract

GECCO is the largest selective conference in the field of Evolutionary Computation, and the main conference of the Special Interest Group on Genetic and Evolutionary Computation (SIGEVO) of the Association for Computing Machinery (ACM). GECCO implements a rigorous and selective reviewing process to identify important and technically sound papers to publish. The technical program is divided into thirteen tracks reflecting all aspects of our field and chaired by experts who make the decisions on accepted papers.

research-article
NSGA-Net: neural architecture search using multi-objective genetic algorithm

This paper introduces NSGA-Net --- an evolutionary approach for neural architecture search (NAS). NSGA-Net is designed with three goals in mind: (1) a procedure considering multiple and conflicting objectives, (2) an efficient procedure balancing ...

research-article
Improvement of code fragment fitness to guide feature construction in XCS

In complex classification problems, constructed features with rich discriminative information can simplify decision boundaries. Code Fragments (CFs) produce GP-tree-like constructed features that can represent decision boundaries effectively in Learning ...

research-article
Population-based ensemble classifier induction for domain adaptation

In classification, the task of domain adaptation is to learn a classifier to classify target data using unlabeled data from the target domain and labeled data from a related, but not identical, source domain. Transfer classifier induction is a common ...

research-article
Investigating recurrent neural network memory structures using neuro-evolution

This paper presents a new algorithm, Evolutionary eXploration of Augmenting Memory Models (EXAMM), which is capable of evolving recurrent neural networks (RNNs) using a wide variety of memory structures, such as Δ-RNN, GRU, LSTM, MGU and UGRNN cells. ...

research-article
Deep neuroevolution of recurrent and discrete world models

Neural architectures inspired by our own human cognitive system, such as the recently introduced world models, have been shown to outperform traditional deep reinforcement learning (RL) methods in a variety of different domains. Instead of the ...

research-article
Evolving controllably difficult datasets for clustering

Synthetic datasets play an important role in evaluating clustering algorithms, as they can help shed light on consistent biases, strengths, and weaknesses of particular techniques, thereby supporting sound conclusions. Despite this, there is a ...

research-article
Open Access
Spatial evolutionary generative adversarial networks

Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse. These pathologies mainly arise from a lack of diversity in their adversarial interactions. Evolutionary generative adversarial networks apply ...

research-article
Adaptive multi-subswarm optimisation for feature selection on high-dimensional classification

Feature space is an important factor influencing the performance of any machine learning algorithm including classification methods. Feature selection aims to remove irrelevant and redundant features that may negatively affect the learning process ...

research-article
Evolving deep neural networks by multi-objective particle swarm optimization for image classification

In recent years, convolutional neural networks (CNNs) have become deeper in order to achieve better classification accuracy in image classification. However, it is difficult to deploy the state-of-the-art deep CNNs for industrial use due to the ...

Contributors
  • Alliance Manchester Business School
  • Polytechnique School
  • Université Libre de Bruxelles

Index Terms

  1. Proceedings of the Genetic and Evolutionary Computation Conference
      Index terms have been assigned to the content through auto-classification.

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      Acceptance Rates

      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%
      YearSubmittedAcceptedRate
      GECCO '1746217839%
      GECCO '1638113736%
      GECCO '16 Companion38113736%
      GECCO '1550518236%
      GECCO '1454418033%
      GECCO Comp '1454418033%
      GECCO '1357020436%
      GECCO '0757726646%
      GECCO '0644620546%
      Overall4,4101,66938%