標(biāo)題: Titlebook: Genetic Programming Theory and Practice XVIII; Wolfgang Banzhaf,Leonardo Trujillo,Bill Worzel Book 2022 The Editor(s) (if applicable) and [打印本頁] 作者: sprawl 時(shí)間: 2025-3-21 17:42
書目名稱Genetic Programming Theory and Practice XVIII影響因子(影響力)
書目名稱Genetic Programming Theory and Practice XVIII影響因子(影響力)學(xué)科排名
書目名稱Genetic Programming Theory and Practice XVIII網(wǎng)絡(luò)公開度
書目名稱Genetic Programming Theory and Practice XVIII網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Genetic Programming Theory and Practice XVIII被引頻次
書目名稱Genetic Programming Theory and Practice XVIII被引頻次學(xué)科排名
書目名稱Genetic Programming Theory and Practice XVIII年度引用
書目名稱Genetic Programming Theory and Practice XVIII年度引用學(xué)科排名
書目名稱Genetic Programming Theory and Practice XVIII讀者反饋
書目名稱Genetic Programming Theory and Practice XVIII讀者反饋學(xué)科排名
作者: 仇恨 時(shí)間: 2025-3-21 21:37
,Grammar-Based Vectorial Genetic Programming for?Symbolic Regression,t also allow vector variables. Also, the model’s abilities are extended to allow operations on vectors, where most vector operations are simply performed component-wise. Additionally, new aggregation functions are introduced that reduce vectors into scalars, allowing the model to extract information作者: 不給啤 時(shí)間: 2025-3-22 01:38
Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming,an be generated, improving the performance of Genetic Programming. Strongly-Typed and Grammar-Guided Genetic Programming are two examples of using domain-knowledge to improve performance of Genetic Programming by preventing solutions that are known to be invalid from ever being added to the populati作者: MIR 時(shí)間: 2025-3-22 05:16
What Can Phylogenetic Metrics Tell us About Useful Diversity in Evolutionary Algorithms?,sured and defined in a multitude of ways. To date, most evolutionary computation research has measured diversity using the richness and/or evenness of a particular genotypic or phenotypic property. While these metrics are informative, we hypothesize that other diversity metrics are more strongly pre作者: Herd-Immunity 時(shí)間: 2025-3-22 10:27
,An Exploration of?Exploration: Measuring the?Ability of?Lexicase Selection to?Find Obscure Pathwaystion. Understanding how selection schemes affect exploitation and exploration within a search space is crucial to tackling increasingly challenging problems. Here, we introduce an “exploration diagnostic” that diagnoses?a selection scheme’s capacity for search space exploration. We use our explorati作者: 向外 時(shí)間: 2025-3-22 16:58 作者: 向外 時(shí)間: 2025-3-22 19:57
,Back to the Future—Revisiting OrdinalGP and Trustable Models After a Decade,st small data sets to reward model generalization. ESSENCE (2009)?[.] extended the OrdinalGP concept to handle imbalanced data by using the SMITS algorithm to rank data records according to their information content to avoid locking into the behavior of heavily sampled data regions but had the disad作者: 大門在匯總 時(shí)間: 2025-3-23 00:58 作者: 異端 時(shí)間: 2025-3-23 02:00 作者: 高腳酒杯 時(shí)間: 2025-3-23 07:50 作者: Individual 時(shí)間: 2025-3-23 10:00 作者: 濕潤 時(shí)間: 2025-3-23 15:46
https://doi.org/10.1007/978-981-16-8113-4Genetic Programming; Genetic Programming Theory; Genetic Programming Applications; Symbolic Regression; 作者: 明確 時(shí)間: 2025-3-23 20:00
Wolfgang Banzhaf,Leonardo Trujillo,Bill WorzelProvides papers describing cutting-edge work on the theory and applications of genetic programming (GP).Offers large-scale, real-world applications (big data) of GP to a variety of problem domains.Pre作者: 接觸 時(shí)間: 2025-3-24 00:23
https://doi.org/10.1057/9780230372412grams into graphs of teams of programs. To date, the framework has been demonstrated on reinforcement learning tasks with stochastic partially observable?state spaces or time series prediction. However, evolving solutions to reinforcement tasks often requires agents to demonstrate/ juggle multiple p作者: single 時(shí)間: 2025-3-24 02:23 作者: DENT 時(shí)間: 2025-3-24 08:51 作者: Mindfulness 時(shí)間: 2025-3-24 14:35
https://doi.org/10.1007/978-3-030-45537-8sured and defined in a multitude of ways. To date, most evolutionary computation research has measured diversity using the richness and/or evenness of a particular genotypic or phenotypic property. While these metrics are informative, we hypothesize that other diversity metrics are more strongly pre作者: 排斥 時(shí)間: 2025-3-24 18:14 作者: Formidable 時(shí)間: 2025-3-24 19:47 作者: Feedback 時(shí)間: 2025-3-25 00:03
,: A Precarious Restoration to “the Real”,st small data sets to reward model generalization. ESSENCE (2009)?[.] extended the OrdinalGP concept to handle imbalanced data by using the SMITS algorithm to rank data records according to their information content to avoid locking into the behavior of heavily sampled data regions but had the disad作者: 充氣女 時(shí)間: 2025-3-25 03:52
Physikalische Begriffsbildungenthem. This allows selection before forming the next generation. Thus avoiding unfit runt Genetic Algorithm individuals, which will themselves have no children. In highly diverse GA populations with strong selection, more than 50% of children need not be created. Even with two parent crossover, in co作者: 精致 時(shí)間: 2025-3-25 10:50 作者: 正面 時(shí)間: 2025-3-25 13:01 作者: Asymptomatic 時(shí)間: 2025-3-25 19:48
,Hume’s Empiricism and Modern Empiricism,ansition of information. Understanding what causes the transitions paves the way to potentially creating a predictive model for the industry. Prediction is one of the essential functions of research; it is challenging to get right; it is of paramount importance when it comes to determining the next 作者: 公理 時(shí)間: 2025-3-25 21:17 作者: 阻撓 時(shí)間: 2025-3-26 00:12 作者: 掙扎 時(shí)間: 2025-3-26 04:38 作者: COM 時(shí)間: 2025-3-26 09:09 作者: 浸軟 時(shí)間: 2025-3-26 12:48
Book 2022ection mechanisms. The book includes several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results..作者: 飛行員 時(shí)間: 2025-3-26 16:47 作者: CHANT 時(shí)間: 2025-3-26 22:06 作者: Incumbent 時(shí)間: 2025-3-27 03:27
raction and Absorption of Modules). GLEAM’s flexible architecture and tunable parameters allow researchers to test different methods related to the generation, propagation, and use of modules in genetic programming.作者: 歡樂中國 時(shí)間: 2025-3-27 07:52
Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming,iduals in the context of Christiansen Grammatical Evolution and Refined-Typed Genetic Programming. We present three new approaches for the population initialization procedure of semantically constrained GP that are more efficient and promote more diversity than traditional Grammatical Evolution.作者: 斜 時(shí)間: 2025-3-27 12:31
What Can Phylogenetic Metrics Tell us About Useful Diversity in Evolutionary Algorithms?,y computation, and (2) these metrics better predict the long-term success of a run of evolutionary computation. We find that, in most cases, phylogenetic metrics behave meaningfully differently from other diversity metrics. Moreover, our results suggest that phylogenetic diversity is indeed a better predictor of success.作者: Erythropoietin 時(shí)間: 2025-3-27 13:44 作者: 重力 時(shí)間: 2025-3-27 18:05 作者: Engaged 時(shí)間: 2025-3-27 23:29 作者: saturated-fat 時(shí)間: 2025-3-28 03:38
Physikalische Begriffsbildungenssover, reduces storage in an N?multi-threaded implementation for a population?M to .0.63M+N, compared to the usual M+2N. Memory efficient crossover achieves 692?billion GP operations per second, 692?giga?GPops, at runtime on a 16?core 3.8?GHz desktop.作者: THROB 時(shí)間: 2025-3-28 07:21
Feature Discovery with Deep Learning Algebra Networks,inant analysis to train the deep learning algebra network. These enhanced algebra networks are trained on ten theoretical classification problems with good performance advances which show a clear statistical performance improvement as network architecture is expanded.作者: 紅腫 時(shí)間: 2025-3-28 14:12 作者: JOT 時(shí)間: 2025-3-28 14:58 作者: florid 時(shí)間: 2025-3-28 22:15
1932-0167 d from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results..978-981-16-8115-8978-981-16-8113-4Series ISSN 1932-0167 Series E-ISSN 1932-0175 作者: 琺瑯 時(shí)間: 2025-3-29 01:35
Finding Simple Solutions to Multi-Task Visual Reinforcement Learning Problems with Tangled Program rk the utilization of . (multiple mutations applied simultaneously for offspring creation) and . (multiple actions per state). Several parameterizations are also introduced that potentially penalize the introduction of hitchhikers. Benchmarking over five VizDoom tasks demonstrates that rampant mutat作者: VICT 時(shí)間: 2025-3-29 06:00 作者: 強(qiáng)制令 時(shí)間: 2025-3-29 09:05
,An Exploration of?Exploration: Measuring the?Ability of?Lexicase Selection to?Find Obscure Pathways we find that relaxing lexicase’s elitism with epsilon lexicase can further improve exploration. Both down-sampling and cohort lexicase—two techniques for applying random subsampling to test cases—degrade lexicase’s exploratory capacity; however, we find that cohort partitioning better preserves lex作者: entitle 時(shí)間: 2025-3-29 13:09
,Back to the Future—Revisiting OrdinalGP and Trustable Models After a Decade,erlying system. Although the deployed implementation has been effective, the diversity metric used was data-centric so alternatives have been explored to improve the robustness of ensemble definition. This chapter documents our latest thinking, realizations, and benefits of revisiting OrdinalGP and 作者: 反感 時(shí)間: 2025-3-29 18:24
Evolution of the Semiconductor Industry, and the Start of X Law, The human appetite to consume more data puts pressure on the industry. Consumption drives three technology vectors, namely storage, compute, and communication. Under this premise, two thoughts lead to this paper. Firstly, the .?(EoML) [.], where transistor density growth slows down over time. Eithe作者: 不幸的人 時(shí)間: 2025-3-29 20:37
