Highlights of Semantics in Multi-objective Genetic Programming

• URL: http://arxiv.org/abs/2206.05010v2
• Date: Mon, 13 Jun 2022 16:11:13 GMT
• Title: Highlights of Semantics in Multi-objective Genetic Programming
• Authors: Edgar Galv\'an, Leonardo Trujillo, Fergal Stapleton
• Abstract summary: This research expands upon the current understanding of semantics in Genetic programming (GP) by proposing a new approach: Semantic-based Distance as an additional criteriOn (SDO) Our work included an expansive analysis of the GP in terms of performance and diversity metrics, using two additional semantic-based approaches, namely Semantic Similarity-based Crossover ( SCC) and Semantic-based Crowding Distance (SCD) Using highly-unbalanced binary classification datasets, we demonstrated that the newly proposed approach of SDO consistently generated more non-dominated solutions, with better diversity and improved hypervolume results.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantics is a growing area of research in Genetic programming (GP) and refers to the behavioural output of a Genetic Programming individual when executed. This research expands upon the current understanding of semantics by proposing a new approach: Semantic-based Distance as an additional criteriOn (SDO), in the thus far, somewhat limited researched area of semantics in Multi-objective GP (MOGP). Our work included an expansive analysis of the GP in terms of performance and diversity metrics, using two additional semantic-based approaches, namely Semantic Similarity-based Crossover (SCC) and Semantic-based Crowding Distance (SCD). Each approach is integrated into two evolutionary multi-objective (EMO) frameworks: Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and along with the three semantic approaches, the canonical form of NSGA-II and SPEA2 are rigorously compared. Using highly-unbalanced binary classification datasets, we demonstrated that the newly proposed approach of SDO consistently generated more non-dominated solutions, with better diversity and improved hypervolume results.

Related papers

• Unified Generative and Discriminative Training for Multi-modal Large Language Models [88.84491005030316]
  Generative training has enabled Vision-Language Models (VLMs) to tackle various complex tasks. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval. This paper proposes a unified approach that integrates the strengths of both paradigms.
  arXiv  Detail & Related papers  (2024-11-01T01:51:31Z)
• Continuous Cartesian Genetic Programming based representation for Multi-Objective Neural Architecture Search [12.545742558041583]
  We propose a novel approach for designing less complex yet highly effective convolutional neural networks (CNNs) Our approach combines real-based and block-chained CNNs representations based on cartesian genetic programming (CGP) for neural architecture search (NAS) Two variants are introduced that differ in the granularity of the search space they consider.
  arXiv  Detail & Related papers  (2023-06-05T07:32:47Z)
• Consistency and Diversity induced Human Motion Segmentation [231.36289425663702]
  We propose a novel Consistency and Diversity induced human Motion (CDMS) algorithm. Our model factorizes the source and target data into distinct multi-layer feature spaces. A multi-mutual learning strategy is carried out to reduce the domain gap between the source and target data.
  arXiv  Detail & Related papers  (2022-02-10T06:23:56Z)
• Group Gated Fusion on Attention-based Bidirectional Alignment for Multimodal Emotion Recognition [63.07844685982738]
  This paper presents a new model named as Gated Bidirectional Alignment Network (GBAN), which consists of an attention-based bidirectional alignment network over LSTM hidden states. We empirically show that the attention-aligned representations outperform the last-hidden-states of LSTM significantly. The proposed GBAN model outperforms existing state-of-the-art multimodal approaches on the IEMOCAP dataset.
  arXiv  Detail & Related papers  (2022-01-17T09:46:59Z)
• Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees [94.72461292387146]
  We propose to reformulate the result diversification problem as a bi-objective search problem, and solve it by a multi-objective evolutionary algorithm (EA) We theoretically prove that the GSEMO can achieve the optimal-time approximation ratio, $1/2$. When the objective function changes dynamically, the GSEMO can maintain this approximation ratio in running time, addressing the open question proposed by Borodin et al.
  arXiv  Detail & Related papers  (2021-10-18T14:00:22Z)
• Semantics in Multi-objective Genetic Programming [0.0]
  We propose SDO: Semantic-based Distance as an additional criteriOn. This naturally encourages semantic diversity in Multi-objective GP. We show how our proposed SDO approach produces more non-volume solutions and better diversity, leading to better statistically significant results.
  arXiv  Detail & Related papers  (2021-05-06T20:43:30Z)
• Semantic Neighborhood Ordering in Multi-objective Genetic Programming based on Decomposition [0.0]
  We show how we can promote semantic diversity in Evolutionary Multi-objective Optimization (EMO) using Multi-objective Evolutionary Algorithms Based on Decomposition (MOEA/D) We show, for the first time, how we can promote semantic diversity in MOEA/D in Genetic Programming.
  arXiv  Detail & Related papers  (2021-02-28T12:18:37Z)
• Promoting Semantics in Multi-objective Genetic Programming based on Decomposition [0.0]
  We show how Semantic Similarity-based Crossover (SSC) in Evolutionary Algorithms based on Decomposition (MOEA/D) promotes semantic diversity yielding better results compared to when this is not present in canonical MOEA/D.
  arXiv  Detail & Related papers  (2020-12-08T20:07:47Z)
• Transformer-based Multi-Aspect Modeling for Multi-Aspect Multi-Sentiment Analysis [56.893393134328996]
  We propose a novel Transformer-based Multi-aspect Modeling scheme (TMM), which can capture potential relations between multiple aspects and simultaneously detect the sentiment of all aspects in a sentence. Our method achieves noticeable improvements compared with strong baselines such as BERT and RoBERTa.
  arXiv  Detail & Related papers  (2020-11-01T11:06:31Z)
• Semantic-based Distance Approaches in Multi-objective Genetic Programming [0.0]
  We conduct a comparison of three different forms of semantics in Multi-objective (MO) GP. One semantic-based method, (i) Semantic Similarity-based Crossover (SSC), is borrowed from single-objective GP. We also study two other methods, dubbed (ii) Semantic-based Distance as an additional criteriOn (SDO) and (iii) Pivot Similarity SDO.
  arXiv  Detail & Related papers  (2020-09-25T19:01:13Z)
• A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning [52.83948119677194]
  We propose a multi-semantic metapath (MSM) model for large scale heterogeneous representation learning. Specifically, we generate multi-semantic metapath-based random walks to construct the heterogeneous neighborhood to handle the unbalanced distributions. We conduct systematical evaluations for the proposed framework on two challenging datasets: Amazon and Alibaba.
  arXiv  Detail & Related papers  (2020-07-19T22:50:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.