Artificial Evolution

s of Invited Talks The Cartography of Computational Search Spaces

[1]  Anthony Brabazon,et al.  Defining locality in genetic programming to predict performance , 2010, IEEE Congress on Evolutionary Computation.

[2]  Lee Spector,et al.  Epsilon-Lexicase Selection for Regression , 2016, GECCO.

[3]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[4]  Anthony Brabazon,et al.  Defining locality as a problem difficulty measure in genetic programming , 2011, Genetic Programming and Evolvable Machines.

[5]  Anthony Brabazon,et al.  Towards an understanding of locality in genetic programming , 2010, GECCO '10.

[6]  Peter Ross,et al.  Dynamic Training Subset Selection for Supervised Learning in Genetic Programming , 1994, PPSN.

[7]  Wolfgang Banzhaf,et al.  Dynamic Subset Selection Based on a Fitness Case Topology , 2004, Evolutionary Computation.

[8]  Leonardo Vanneschi,et al.  A Study of Genetic Programming Variable Population Size for Dynamic Optimization Problems , 2009, IJCCI.

[9]  Andries Petrus Engelbrecht,et al.  Adaptive Genetic Programming for dynamic classification problems , 2009, 2009 IEEE Congress on Evolutionary Computation.

[10]  Terry Jones,et al.  Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms , 1995, ICGA.

[11]  Leonardo Vanneschi,et al.  Limiting the Number of Fitness Cases in Genetic Programming Using Statistics , 2002, PPSN.

[12]  Leonardo Trujillo,et al.  Stochastic Semantic-Based Multi-objective Genetic Programming Optimisation for Classification of Imbalanced Data , 2016, MICAI.

[13]  Efrén Mezura-Montes,et al.  On the Use of Semantics in Multi-objective Genetic Programming , 2016, PPSN.

[14]  Ahmed Kattan,et al.  Locality in Continuous Fitness-Valued Cases and Genetic Programming Difficulty , 2012, EVOLVE.

[15]  Leonardo Trujillo,et al.  Dynamic GP fitness cases in static and dynamic optimisation problems , 2017, GECCO.

[16]  Shengxiang Yang,et al.  Evolutionary dynamic optimization: A survey of the state of the art , 2012, Swarm Evol. Comput..

[17]  Leonardo Trujillo,et al.  A comparison of fitness-case sampling methods for genetic programming , 2017, J. Exp. Theor. Artif. Intell..

[18]  Astro Teller,et al.  Automatically Choosing the Number of Fitness Cases: The Rational Allocation of Trials , 1997 .

[19]  Lino Marques,et al.  Genetic Programming Algorithms for Dynamic Environments , 2016, EvoApplications.

[20]  Ivo Gonçalves,et al.  Balancing Learning and Overfitting in Genetic Programming with Interleaved Sampling of Training Data , 2013, EuroGP.

[21]  Edgar Galván López,et al.  Using fitness comparison disagreements as a metric for promoting diversity in Dynamic Optimisation Problems , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[22]  Leonardo Trujillo,et al.  RANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming , 2017, EuroGP.

[23]  Lee Spector,et al.  Assessment of problem modality by differential performance of lexicase selection in genetic programming: a preliminary report , 2012, GECCO '12.

[24]  Leonardo Vanneschi,et al.  Genetic programming needs better benchmarks , 2012, GECCO '12.

[25]  Zbigniew Michalewicz,et al.  Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model , 2007, IEEE Transactions on Evolutionary Computation.