{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/evolutionary-algorithms/papers/10","list_of":"/task/evolutionary-algorithms","task":"Evolutionary Algorithms","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":10,"pages_in_order":12,"rows_per_page":100,"rows":[901,1000],"of":1107,"counts":{"archive_papers_tagged":1107,"with_a_code_link":241,"where_syntology_ran_a_sample":36,"not_listed_spam_title":0,"listed":1107,"listed_where_code_ran":36,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":32,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":32,"listed_every_run_a_failure_of_syntologys_instrument":4,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/evolutionary-algorithms","prev":"/task/evolutionary-algorithms/papers/9","next":"/task/evolutionary-algorithms/papers/11","papers":[{"url":null,"slug":"towards-a-theory-guided-benchmarking-suite","title":"Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling $(1+λ)$ EA Variants on OneMax and LeadingOnes","date":"2018-08-17","arxiv_id":"1808.05850","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-linear-hidden-subset-problem-for-the-11","title":"The linear hidden subset problem for the (1+1) EA with scheduled and adaptive mutation rates","date":"2018-08-16","arxiv_id":"1808.05566","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-optimisation-of-neural-network","title":"Evolutionary optimisation of neural network models for fish collective behaviours in mixed groups of robots and zebrafish","date":"2018-08-09","arxiv_id":"1808.03166","repositories_listed":0,"syntology":null},{"url":null,"slug":"robot-imitation-through-vision-kinesthetic","title":"Robot Imitation through Vision, Kinesthetic and Force Features with Online Adaptation to Changing Environments","date":"2018-07-24","arxiv_id":"1807.09177","repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-perspective-of-convergence","title":"Theoretical Perspective of Convergence Complexity of Evolutionary Algorithms Adopting Optimal Mixing","date":"2018-07-24","arxiv_id":"1807.09203","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-many-objective-evolutionary-algorithm-based","title":"A Many-Objective Evolutionary Algorithm Based on Decomposition and Local Dominance","date":"2018-07-13","arxiv_id":"1807.10275","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-parameter-choices-via-precise-black","title":"Optimal Parameter Choices via Precise Black-Box Analysis","date":"2018-07-09","arxiv_id":"1807.03403","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-first-analysis-of-kernels-for-kriging-based","title":"A First Analysis of Kernels for Kriging-based Optimization in Hierarchical Search Spaces","date":"2018-07-03","arxiv_id":"1807.01011","repositories_listed":0,"syntology":null},{"url":null,"slug":"expanding-variational-autoencoders-for","title":"Expanding variational autoencoders for learning and exploiting latent representations in search distributions","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-decomposition-based-many-objective","title":"A Decomposition-Based Many-Objective Evolutionary Algorithm with Local Iterative Update","date":"2018-06-27","arxiv_id":"1806.10950","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-evolutionary-algorithms-in","title":"Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments","date":"2018-06-22","arxiv_id":"1806.08547","repositories_listed":0,"syntology":null},{"url":null,"slug":"theory-of-estimation-of-distribution","title":"Theory of Estimation-of-Distribution Algorithms","date":"2018-06-14","arxiv_id":"1806.05392","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-evolutionary-algorithms-for","title":"Benchmarking Evolutionary Algorithms For Single Objective Real-valued Constrained Optimization - A Critical Review","date":"2018-06-12","arxiv_id":"1806.04563","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-parallel-portfolio-selection-with","title":"Online Parallel Portfolio Selection with Heterogeneous Island Model","date":"2018-06-12","arxiv_id":"1806.04528","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-enhanced-bpso-based-approach-for-service","title":"An Enhanced Binary Particle-Swarm Optimization (E-BPSO) Algorithm for Service Placement in Hybrid Cloud Platforms","date":"2018-06-10","arxiv_id":"1806.05971","repositories_listed":0,"syntology":null},{"url":null,"slug":"gp-rvm-genetic-programing-based-symbolic","title":"GP-RVM: Genetic Programing-based Symbolic Regression Using Relevance Vector Machine","date":"2018-06-07","arxiv_id":"1806.02502","repositories_listed":0,"syntology":null},{"url":null,"slug":"new-hybrid-neuro-evolutionary-algorithms-for","title":"New Hybrid Neuro-Evolutionary Algorithms for Renewable Energy and Facilities Management Problems","date":"2018-06-05","arxiv_id":"1806.02654","repositories_listed":0,"syntology":null},{"url":null,"slug":"precise-runtime-analysis-for-plateaus","title":"Precise Runtime Analysis for Plateau Functions","date":"2018-06-04","arxiv_id":"1806.01331","repositories_listed":0,"syntology":null},{"url":null,"slug":"ring-migration-topology-helps-bypassing-local","title":"Ring Migration Topology Helps Bypassing Local Optima","date":"2018-06-04","arxiv_id":"1806.01128","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-aggressive-genetic-programming-approach","title":"An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints","date":"2018-06-03","arxiv_id":"1806.00851","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-immune-systems-can-find","title":"Artificial Immune Systems Can Find Arbitrarily Good Approximations for the NP-Hard Number Partitioning Problem","date":"2018-06-01","arxiv_id":"1806.00300","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-artificial-immune-systems","title":"Fast Artificial Immune Systems","date":"2018-06-01","arxiv_id":"1806.00299","repositories_listed":0,"syntology":null},{"url":null,"slug":"sample-reuse-via-importance-sampling-in","title":"Sample Reuse via Importance Sampling in Information Geometric Optimization","date":"2018-05-31","arxiv_id":"1805.12388","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-blend-a-robot-within-a-group-of","title":"How to Blend a Robot within a Group of Zebrafish: Achieving Social Acceptance through Real-time Calibration of a Multi-level Behavioural Model","date":"2018-05-29","arxiv_id":"1805.11371","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-algorithms","title":"Evolutionary Algorithms","date":"2018-05-28","arxiv_id":"1805.11014","repositories_listed":0,"syntology":null},{"url":null,"slug":"design-and-analysis-of-diversity-based-parent","title":"Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation","date":"2018-05-03","arxiv_id":"1805.01221","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiobjective-optimization-differential","title":"Multiobjective Optimization Differential Evolution Enhanced with Principle Component Analysis for Constrained Optimization","date":"2018-05-01","arxiv_id":"1805.00272","repositories_listed":0,"syntology":null},{"url":null,"slug":"limited-evaluation-cooperative-co","title":"Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution","date":"2018-04-19","arxiv_id":"1804.07234","repositories_listed":0,"syntology":null},{"url":null,"slug":"memetic-algorithms-beat-evolutionary","title":"Memetic Algorithms Beat Evolutionary Algorithms on the Class of Hurdle Problems","date":"2018-04-17","arxiv_id":"1804.06173","repositories_listed":0,"syntology":null},{"url":null,"slug":"theory-of-parameter-control-for-discrete","title":"Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices","date":"2018-04-16","arxiv_id":"1804.05650","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-asynchronous-non-dominated-sorting-for","title":"On Asynchronous Non-Dominated Sorting for Steady-State Multiobjective Evolutionary Algorithms","date":"2018-04-14","arxiv_id":"1804.05208","repositories_listed":0,"syntology":null},{"url":null,"slug":"composing-photomosaic-images-using-clustering","title":"Composing photomosaic images using clustering based evolutionary programming","date":"2018-04-09","arxiv_id":"1804.02827","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-hypermutations-and-ageing-enable","title":"When Hypermutations and Ageing Enable Artificial Immune Systems to Outperform Evolutionary Algorithms","date":"2018-04-04","arxiv_id":"1804.01314","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-general-dichotomy-of-evolutionary","title":"A General Dichotomy of Evolutionary Algorithms on Monotone Functions","date":"2018-03-25","arxiv_id":"1803.09227","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-n-level-hypergraph-partitioning","title":"Evolutionary n-level Hypergraph Partitioning with Adaptive Coarsening","date":"2018-03-25","arxiv_id":"1803.09258","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-neural-architecture-construction-using","title":"Fast Neural Architecture Construction using EnvelopeNets","date":"2018-03-18","arxiv_id":"1803.06744","repositories_listed":0,"syntology":null},{"url":null,"slug":"policy-search-in-continuous-action-domains-an","title":"Policy Search in Continuous Action Domains: an Overview","date":"2018-03-13","arxiv_id":"1803.04706","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-evolutionary-conversion-rate","title":"Enhancing Evolutionary Conversion Rate Optimization via Multi-armed Bandit Algorithms","date":"2018-03-10","arxiv_id":"1803.03737","repositories_listed":0,"syntology":null},{"url":null,"slug":"igd-indicator-based-evolutionary-algorithm","title":"IGD Indicator-based Evolutionary Algorithm for Many-objective Optimization Problems","date":"2018-02-24","arxiv_id":"1802.08792","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-regularity-model-based-eda-for-many","title":"Improved Regularity Model-based EDA for Many-objective Optimization","date":"2018-02-24","arxiv_id":"1802.08788","repositories_listed":0,"syntology":null},{"url":null,"slug":"discrepancy-based-evolutionary-diversity","title":"Discrepancy-based Evolutionary Diversity Optimization","date":"2018-02-15","arxiv_id":"1802.05448","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-runtime-bounds-for-the-univariate","title":"Improved Runtime Bounds for the Univariate Marginal Distribution Algorithm via Anti-Concentration","date":"2018-02-02","arxiv_id":"1802.00721","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-benefits-of-population-diversity-in","title":"The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses","date":"2018-01-30","arxiv_id":"1801.10087","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-signals-in-cognitive-radio-systems","title":"Denoising Signals in Cognitive Radio Systems Using An Evolutionary Algorithm Based Adaptive Filter","date":"2018-01-29","arxiv_id":"1801.09724","repositories_listed":0,"syntology":null},{"url":null,"slug":"noise-cancellation-in-cognitive-radio-systems","title":"Noise Cancellation in Cognitive Radio Systems: A Performance Comparison of Evolutionary Algorithms","date":"2018-01-29","arxiv_id":"1801.09725","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-mullti-or-many-objective-evolutionary","title":"A mullti- or many- objective evolutionary algorithm with global loop update","date":"2018-01-25","arxiv_id":"1803.06282","repositories_listed":0,"syntology":null},{"url":null,"slug":"gitgraph-architecture-search-space-creation","title":"GitGraph - Architecture Search Space Creation through Frequent Computational Subgraph Mining","date":"2018-01-16","arxiv_id":"1801.05159","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-runtime-guarantees-via-stochastic","title":"Better Runtime Guarantees Via Stochastic Domination","date":"2018-01-13","arxiv_id":"1801.04487","repositories_listed":0,"syntology":null},{"url":null,"slug":"complexity-theory-for-discrete-black-box","title":"Complexity Theory for Discrete Black-Box Optimization Heuristics","date":"2018-01-06","arxiv_id":"1801.02037","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-expectation-maximization-for","title":"Evolutionary Expectation Maximization for Generative Models with Binary Latents","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evolving-unsupervised-deep-neural-networks","title":"Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations","date":"2017-12-13","arxiv_id":"1712.05043","repositories_listed":0,"syntology":null},{"url":null,"slug":"drift-analysis","title":"Drift Analysis","date":"2017-12-04","arxiv_id":"1712.00964","repositories_listed":0,"syntology":null},{"url":null,"slug":"maximizing-non-monotonenon-submodular","title":"Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms","date":"2017-11-20","arxiv_id":"1711.07214","repositories_listed":0,"syntology":null},{"url":null,"slug":"concurrent-pump-scheduling-and-storage-level","title":"Concurrent Pump Scheduling and Storage Level Optimization Using Meta-Models and Evolutionary Algorithms","date":"2017-11-14","arxiv_id":"1711.04988","repositories_listed":0,"syntology":null},{"url":null,"slug":"running-time-analysis-of-the-11-ea-for-onemax","title":"Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise","date":"2017-11-02","arxiv_id":"1711.00956","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-many-objective-evolutionary-algorithm-with-1","title":"A Many-Objective Evolutionary Algorithm with Angle-Based Selection and Shift-Based Density Estimation","date":"2017-09-30","arxiv_id":"1710.00175","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-compression-for-sentiment-analysis-via","title":"Text Compression for Sentiment Analysis via Evolutionary Algorithms","date":"2017-09-20","arxiv_id":"1709.06990","repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-analysis-of-stochastic-search","title":"Theoretical Analysis of Stochastic Search Algorithms","date":"2017-09-04","arxiv_id":"1709.00890","repositories_listed":0,"syntology":null},{"url":null,"slug":"algorithmically-probable-mutations-reproduce","title":"Algorithmically probable mutations reproduce aspects of evolution such as convergence rate, genetic memory, and modularity","date":"2017-09-01","arxiv_id":"1709.00268","repositories_listed":0,"syntology":null},{"url":null,"slug":"standard-steady-state-genetic-algorithms-can","title":"Standard Steady State Genetic Algorithms Can Hillclimb Faster than Mutation-only Evolutionary Algorithms","date":"2017-08-04","arxiv_id":"1708.01571","repositories_listed":0,"syntology":null},{"url":null,"slug":"preselection-via-classification-a-case-study","title":"Preselection via Classification: A Case Study on Evolutionary Multiobjective Optimization","date":"2017-08-03","arxiv_id":"1708.01146","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-harmony-search-based-wrapper-feature","title":"A Harmony Search Based Wrapper Feature Selection Method for Holistic Bangla word Recognition","date":"2017-07-26","arxiv_id":"1707.08398","repositories_listed":0,"syntology":null},{"url":null,"slug":"ideological-sublations-resolution-of","title":"Ideological Sublations: Resolution of Dialectic in Population-based Optimization","date":"2017-07-21","arxiv_id":"1707.06992","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-the-parameter-space-of","title":"Investigating the Parameter Space of Evolutionary Algorithms","date":"2017-06-13","arxiv_id":"1706.04119","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-convergence-of-the-11-evolution","title":"Global Convergence of the (1+1) Evolution Strategy","date":"2017-06-09","arxiv_id":"1706.02887","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-on-bilevel-optimization-from","title":"A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications","date":"2017-05-17","arxiv_id":"1705.06270","repositories_listed":0,"syntology":null},{"url":null,"slug":"metaheuristic-design-of-feedforward-neural","title":"Metaheuristic Design of Feedforward Neural Networks: A Review of Two Decades of Research","date":"2017-05-16","arxiv_id":"1705.05584","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-learning-of-fire-fighting","title":"Evolutionary learning of fire fighting strategies","date":"2017-05-04","arxiv_id":"1705.01721","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-read-many-objective-solution-sets-in","title":"How to Read Many-Objective Solution Sets in Parallel Coordinates","date":"2017-04-30","arxiv_id":"1705.00368","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-tribe-competition-based-genetic-algorithm","title":"A Tribe Competition-Based Genetic Algorithm for Feature Selection in Pattern Classification","date":"2017-04-28","arxiv_id":"1704.08818","repositories_listed":0,"syntology":null},{"url":null,"slug":"genealogical-distance-as-a-diversity-estimate","title":"Genealogical Distance as a Diversity Estimate in Evolutionary Algorithms","date":"2017-04-27","arxiv_id":"1704.08774","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-vanilla-rolling-horizon-evolution","title":"Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing","date":"2017-04-24","arxiv_id":"1704.07075","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-popperian-falsification-of-artificial","title":"A Popperian Falsification of Artificial Intelligence -- Lighthill Defended","date":"2017-04-23","arxiv_id":"1704.08111","repositories_listed":0,"syntology":null},{"url":null,"slug":"population-seeding-techniques-for-rolling","title":"Population Seeding Techniques for Rolling Horizon Evolution in General Video Game Playing","date":"2017-04-23","arxiv_id":"1704.06942","repositories_listed":0,"syntology":null},{"url":null,"slug":"runtime-analysis-of-the-1-genetic-algorithm","title":"Runtime Analysis of the $(1+(λ,λ))$ Genetic Algorithm on Random Satisfiable 3-CNF Formulas","date":"2017-04-14","arxiv_id":"1704.04366","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximating-optimization-problems-using-eas","title":"Approximating Optimization Problems using EAs on Scale-Free Networks","date":"2017-04-12","arxiv_id":"1704.03664","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-1-evolutionary-algorithm-with-self","title":"The (1+$λ$) Evolutionary Algorithm with Self-Adjusting Mutation Rate","date":"2017-04-07","arxiv_id":"1704.02191","repositories_listed":0,"syntology":null},{"url":null,"slug":"experimental-analysis-of-design-elements-of","title":"Experimental Analysis of Design Elements of Scalarizing Functions-based Multiobjective Evolutionary Algorithms","date":"2017-03-28","arxiv_id":"1703.09469","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-adaptive-framework-to-tune-the-coordinate","title":"An Adaptive Framework to Tune the Coordinate Systems in Evolutionary Algorithms","date":"2017-03-18","arxiv_id":"1703.06263","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-image-composition-using-feature","title":"Evolutionary Image Composition Using Feature Covariance Matrices","date":"2017-03-10","arxiv_id":"1703.03773","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-evolutionary-algorithms-for-search","title":"Beyond Evolutionary Algorithms for Search-based Software Engineering","date":"2017-01-27","arxiv_id":"1701.07950","repositories_listed":0,"syntology":null},{"url":null,"slug":"subpopulation-diversity-based-selecting","title":"Subpopulation Diversity Based Selecting Migration Moment in Distributed Evolutionary Algorithms","date":"2017-01-05","arxiv_id":"1701.01271","repositories_listed":0,"syntology":null},{"url":null,"slug":"platemo-a-matlab-platform-for-evolutionary","title":"PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization","date":"2017-01-04","arxiv_id":"1701.00879","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-of-test-case-generation-using","title":"Optimization of Test Case Generation using Genetic Algorithm (GA)","date":"2016-12-28","arxiv_id":"1612.08813","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-combinatorial-optimization-problems","title":"Solving Combinatorial Optimization problems with Quantum inspired Evolutionary Algorithm Tuned using a Novel Heuristic Method","date":"2016-12-23","arxiv_id":"1612.08109","repositories_listed":0,"syntology":null},{"url":null,"slug":"difficulty-adjustable-and-scalable","title":"Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit","date":"2016-12-21","arxiv_id":"1612.07603","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-quick-hypervolume-algorithm","title":"Improved Quick Hypervolume Algorithm","date":"2016-12-11","arxiv_id":"1612.03402","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-differentiable-physics-engine-for-deep","title":"A Differentiable Physics Engine for Deep Learning in Robotics","date":"2016-11-05","arxiv_id":"1611.01652","repositories_listed":0,"syntology":null},{"url":null,"slug":"surrogate-assisted-partial-order-based","title":"Surrogate-Assisted Partial Order-based Evolutionary Optimisation","date":"2016-11-01","arxiv_id":"1611.00260","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-the-effects-diversity","title":"Investigating the effects Diversity Mechanisms have on Evolutionary Algorithms in Dynamic Environments","date":"2016-10-09","arxiv_id":"1610.02732","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-constraint-handling-technique-for-genetic","title":"A Constraint-Handling Technique for Genetic Algorithms using a Violation Factor","date":"2016-10-04","arxiv_id":"1610.00976","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-opposites-using-neural-networks","title":"Learning Opposites Using Neural Networks","date":"2016-09-16","arxiv_id":"1609.05123","repositories_listed":0,"syntology":null},{"url":null,"slug":"modelling-cyber-security-experts-decision","title":"Modelling Cyber-Security Experts' Decision Making Processes using Aggregation Operators","date":"2016-08-30","arxiv_id":"1608.08497","repositories_listed":0,"syntology":null},{"url":null,"slug":"haploid-diploid-evolutionary-algorithms","title":"Haploid-Diploid Evolutionary Algorithms","date":"2016-08-19","arxiv_id":"1608.05578","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-approaches-to-optimization","title":"Evolutionary Approaches to Optimization Problems in Chimera Topologies","date":"2016-08-17","arxiv_id":"1608.05105","repositories_listed":0,"syntology":null},{"url":null,"slug":"students-t-distribution-based-estimation-of","title":"Student's t Distribution based Estimation of Distribution Algorithms for Derivative-free Global Optimization","date":"2016-08-12","arxiv_id":"1608.03757","repositories_listed":0,"syntology":null},{"url":null,"slug":"drift-analysis-and-evolutionary-algorithms","title":"Drift Analysis and Evolutionary Algorithms Revisited","date":"2016-08-10","arxiv_id":"1608.03226","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-evolutionary-process-of-image-transition","title":"The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation","date":"2016-08-05","arxiv_id":"1608.01783","repositories_listed":0,"syntology":null},{"url":null,"slug":"mpead-multi-population-ea-diagrams","title":"mpEAd: Multi-Population EA Diagrams","date":"2016-07-18","arxiv_id":"1607.05213","repositories_listed":0,"syntology":null},{"url":null,"slug":"populations-can-be-essential-in-tracking","title":"Populations can be essential in tracking dynamic optima","date":"2016-07-12","arxiv_id":"1607.03317","repositories_listed":0,"syntology":null}],"record_sha256":"503206f0eafe0d87d98a2ee6a2caa1c8f6fcf779dcd192b7951f658e44175c1e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}