How long should offspring lifespan be in order to obtain a proper exploration?

The time an offspring should live and remain into the population in order to evolve and mature is a crucial factor of the performance of population-based algorithms both in the search for global optima, and in escaping from the local optima. Offsprings lifespan influences a correct exploration of the search space, and a fruitful exploiting of the knowledge learned. In this research work we present an experimental study on an immunological-inspired heuristic, called OPT-IA, with the aim to understand how long must the lifespan of each clone be to properly explore the solution space. Eleven different types of age assignment have been considered and studied, for an overall of 924 experiments, with the main goal to determine the best one, as well as an efficiency ranking among all the age assignments. This research work represents a first step towards the verification if the top 4 age assignments in the obtained ranking are still valid and suitable on other discrete and continuous domains, i.e. they continue to be the top 4 even if in different order.

[1]  Leandro dos Santos Coelho,et al.  Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems , 2018, Int. J. Bio Inspired Comput..

[2]  Andries Petrus Engelbrecht,et al.  Application of the feature-detection rule to the Negative Selection Algorithm , 2013, Expert Syst. Appl..

[3]  Zhihua Cui,et al.  Monarch butterfly optimization , 2015, Neural Computing and Applications.

[4]  Vincenzo Cutello,et al.  Aligning Multiple Protein Sequences by Hybrid Clonal Selection Algorithm with Insert-Remove-Gaps and BlockShuffling Operators , 2006, ICARIS.

[5]  Vincenzo Cutello,et al.  An Immune Algorithm for Protein Structure Prediction on Lattice Models , 2007, IEEE Transactions on Evolutionary Computation.

[6]  Vincenzo Cutello,et al.  An immune algorithm with stochastic aging and kullback entropy for the chromatic number problem , 2007, J. Comb. Optim..

[7]  A. Prügel-Bennett,et al.  Modelling genetic algorithm dynamics , 2001 .

[8]  Vincenzo Cutello,et al.  On discrete models and immunological algorithms for protein structure prediction , 2011, Natural Computing.

[9]  S. Deb,et al.  Elephant Herding Optimization , 2015, 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI).

[10]  Vincenzo Cutello,et al.  Protein multiple sequence alignment by hybrid bio-inspired algorithms , 2011, Nucleic acids research.

[11]  Gaige Wang,et al.  Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems , 2016, Memetic Computing.

[12]  Jon Timmis,et al.  An immune network inspired evolutionary algorithm for the diagnosis of Parkinson's disease , 2008, Biosyst..

[13]  Giuseppe Nicosia,et al.  Clonal selection: an immunological algorithm for global optimization over continuous spaces , 2012, J. Glob. Optim..

[14]  Vincenzo Cutello,et al.  Escaping Local Optima via Parallelization and Migration , 2013, NICSO.

[15]  Vincenzo Cutello,et al.  An Information-Theoretic Approach for Clonal Selection Algorithms , 2010, ICARIS.

[16]  Vincenzo Cutello,et al.  Packing equal disks in a unit square: an immunological optimization approach , 2015, 2015 International Workshop on Artificial Immune Systems (AIS).

[17]  Vincenzo Cutello,et al.  Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials , 2005, ICARIS.

[18]  Vincenzo Cutello,et al.  Clonal Selection Algorithm with Dynamic Population Size for Bimodal Search Spaces , 2006, ICNC.