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10.1117/12.2601117

To solve this issue, various solutions on the automatic construction of CNN architectures are proposed, including but not limited to a genetic algorithm AE-CNN 1 for automatically evolving CNN design using ResNet and DenseNet blocks.

Fully automatic CNN design with inception blocks

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10.1162/evco_a_00264

In this article, we propose a new approach to automatically evolving similarity functions for a given clustering algorithm by using genetic programming.

Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis

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10.1145/3321707.3321790

Genetic programming hyper-heuristic (GPHH) has been widely used for automatically evolving the routing and sequencing rules for DFJSS.

A two-stage genetic programming hyper-heuristic approach with feature selection for dynamic flexible job shop scheduling

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10.1016/j.ifacol.2019.11.111

The generated model is based on a Petri Net representation of the production process, which we slightly enhance to separate automatically evolving processes from consciously taken manufacturing decisions.

A Petri Net Modeling Framework for the Control of Flexible Manufacturing Systems

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10.1109/CIG.2019.8848117

In this work, we present a way of automatically evolving the weights of these evaluation functions in order to improve the efficiency of the execution of high-level tasks.

Evolving Game State Evaluation Functions for a Hybrid Planning Approach

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10.1109/ACCESS.2019.2897405

Therefore, we propose a method of automatically evolving feature models of software product lines with evolutionary requirements to solve the repeated reconstruction and reduce errors in reconstruction.

A Method of Automatically Evolving Feature Models of Software Product Lines

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10.1145/3319619.3321988

Genetic Programming (GP) has shown effectiveness in automatically evolving routing policies to handle the uncertain environment in UCARP.

Genetic programming hyper-heuristic with knowledge transfer for uncertain capacitated arc routing problem

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Automatically Evolving
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What is/are Automatically Evolving?

Automatically Evolving - To solve this issue, various solutions on the automatic construction of CNN architectures are proposed, including but not limited to a genetic algorithm AE-CNN 1 for automatically evolving CNN design using ResNet and DenseNet blocks. [1] In this article, we propose a new approach to automatically evolving similarity functions for a given clustering algorithm by using genetic programming. [2] Genetic programming hyper-heuristic (GPHH) has been widely used for automatically evolving the routing and sequencing rules for DFJSS. [3] The generated model is based on a Petri Net representation of the production process, which we slightly enhance to separate automatically evolving processes from consciously taken manufacturing decisions. [4] In this work, we present a way of automatically evolving the weights of these evaluation functions in order to improve the efficiency of the execution of high-level tasks. [5] Therefore, we propose a method of automatically evolving feature models of software product lines with evolutionary requirements to solve the repeated reconstruction and reduce errors in reconstruction. [6] Genetic Programming (GP) has shown effectiveness in automatically evolving routing policies to handle the uncertain environment in UCARP. [7]