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.
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 ...
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 ...
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 ...
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. ...
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 ...
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 ...
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 ...
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 ...
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 ...
Cited By
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Sun M, Jiang G, Zhang Y, Wu H and Li H (2024). Covariance matrix adaptation MAP-Elites for video game level generation International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 10.1117/12.3026544, 9781510677685, (106)
- Marrero A, Segredo E, León C and Hart E A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem Parallel Problem Solving from Nature – PPSN XVII, (223-236)
Index Terms
- Proceedings of the Genetic and Evolutionary Computation Conference
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Acceptance Rates
Year | Submitted | Accepted | Rate |
---|---|---|---|
GECCO '17 | 462 | 178 | 39% |
GECCO '16 | 381 | 137 | 36% |
GECCO '16 Companion | 381 | 137 | 36% |
GECCO '15 | 505 | 182 | 36% |
GECCO '14 | 544 | 180 | 33% |
GECCO Comp '14 | 544 | 180 | 33% |
GECCO '13 | 570 | 204 | 36% |
GECCO '07 | 577 | 266 | 46% |
GECCO '06 | 446 | 205 | 46% |
Overall | 4,410 | 1,669 | 38% |