Dynamic function optimisation with hybridised extremal dynamics

Angeline PJ (1997) Tracking extrema in dynamic environments. In: Proceedings of the 6th international conference on evolutionary programming VI, LNCS, vol 1213. Springer, Berlin, pp 335–345

Ayvaz D, Topcuoglu H, Gurgen F (2006) A comparative study of evolutionary optimisation techniques in dynamic environments. In: Proceedings of the genetic and evolutionary computation conference (GECCO 2006), Seattle, pp 1397–1398

Bak P, Sneppen K (1993) Punctuated equilibrium and criticality in a simple model of evolution. Phys Rev Lett 74: 4083–4086

Balle P, Fischer M, Fussel D, Nelles O, Isermann R (1998) Integrated control, diagnosis and reconfiguration of a heat exchanger. IEEE Control Syst Mag 18: 52–63

Benasla L, Belmadani A, Rahli M (2008) Hooke-jeeves’ method applied to a new economic dispatch problem formulation. J Inf Sci Eng 24: 907–917

Blackwell T (2003) Swarms in dynamic environments. In: Proceedings of the genetic and evolutionary computation conference (GECCO 2003), Chicago, pp 1–12

Blackwell T, Branke J (2004) Multi-swarm optimization in dynamic environments. In: Applications of evolutionary computing, LNCS, vol 3005. Springer, Berlin, pp 489–500

Blackwell T, Branke J (2006) Multi-swarms, exclusion and anti-convergence in dynamic environments. IEEE Trans on Evol Comput 10(4): 51–58

Boettcher S (1999) Extremal optimization of graph partitioning at the percolation threshold. J Phys A Math Gen 32: 5201–5211

Boettcher S, Percus A (2000) Nature’s way of optimizing. Artif Intell 119(1): 275–286

Boettcher S, Percus AG (1999) Extremal optimization: Methods derived from co-evolution. In: Proceedings of the genetic and evolutionary computation conference (GECCO 1999), Orlando, FL, pp 825–832

Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the IEEE congress on evolutionary computation (CEC 1999), Washington, DC, pp 1875–1882

Branke J, Schmeck H (2002) Designing evolutionary algorithms for dynamic optimization problems. In: Tsutsui S, Ghosh A (eds) Theory and application of evolutionary computation: recent trends. Springer, Berlin, pp 239–362

Branke J, Kaußler T, Schmidt C, Schmeck H (2000) A multi-population approach to dynamic optimization problems. In: Parmee I (eds) Adaptive computing in design and manufacturing (ACDM 2000). Springer, Berlin, pp 299–308

Bui L, Branke J, Abbass H (2005) Diversity as a selection pressure in dynamic environments. In: Proceedings of the genetic and evolutionary computation conference (GECCO 2005), Washington, DC, pp 1557–1558

Bui L, Branke J, Abbass H (2005) Multiobjective optimization for dynamic environments. In: Proceedings of the IEEE congress on evolutionary computation (CEC 2005), Edinburgh, pp 2349–2356

Grefenstette JJ (1999) Evolvability in dynamic fitness landscapes: a genetic algorithm approach. In: Proceedings of the IEEE congress on evolutionary computation (CEC 1999), Washington, DC, pp 2031–2038

Hooke R, Jeeves T (1961) Direct search solutions of numerical and statistical problems. J Assoc Comput Mach 8: 212–229

Janson S, Middendorf M (2004) A hierachical particle swarm optimizer for dynamic optimization problems. In: Applications of evolutionary computing, LNCS, vol 3005. Springer, Berlin, pp 513–524

De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan

Li J, Balazs M, Parks G, Clarkson P (2002) A species conserving genetic algorithm for multimodal function optimization. Evol Comput 10(3): 207–234

Li X (2004) Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization. In: Proceedings of the genetic and evolutionary computation conference (GECCO 2004), Seattle, pp 105–116

Li X, Branke J, Blackwell T (2006) Particle swarm with speciation and adaptation in a dynamic environment. In: Proceedings of the genetic and evolutionary computation conference (GECCO 2006), Seattle, pp 51–58

Lozano M, Herrera F, Krasnogor N, Molina D (2004) Real-coded memetic algorithms with crossover hill-climbing. Evol Comput 12: 273–302

Lung R, Dumitrescu D (2007) A collaborative model for tracking optima in dynamic environments. In: Proceedings of the IEEE congress on evolutionary computation (CEC 2007), Singapore, pp 564–567

Mendes R, Mohais A (2005) Dynde: A differential evolution for dynamic optimization problems. In: Proceedings of the IEEE congress on evolutionary computation (CEC 2005), Edinburgh, pp 2808–2815

Meyer K, Nasut S, Bishop M (2006) Stochastic diffusion search: partial function evaluation in swarm intelligence dynamic optimization. In: Abraham A, Grosan C, Ramos V (eds) Stigmergic optimization, studies in computational intelligence, vol 31. Springer, Berlin, pp 185–207

Moser I (2008) Applying extremal optimisation to dynamic optimisation problems. PhD thesis, Swinburne University of Technology, Australia

Moser I (2009) Hooke–jeeves revisited. In: Proceedings of the IEEE congress on evolutionary computation (CEC 2009), Trondheim, pp 2670–2676

Moser I, Chiong R (2009) A Hooke–Jeeves based memetic algorithm for solving dynamic optimisation problems. In: Proceedings of the 4th international conference on hybrid artificial intelligence systems (HAIS 2009), LNAI, vol 5572. Springer, Berlin, pp 301–309

Moser I, Hendtlass T (2007) A simple and efficient multi- component algorithm for solving dynamic function optimisation problems. In: Proceedings of the IEEE congress on evolutionary computation (CEC 2007), Singapore, pp 252–259

Neri F, del Toro Garcia X, Cascella GL, Salvatore N (2008) Surrogate assisted local search on pmsm drive design. COMPEL Intl J Comput Math Electr Electr Eng 27(3): 573–592

Parrott D, Li X (2004) A particle swarm model for tracking multiple peaks in a dynamic environment using speciation. In: Proceedings of the IEEE congress on evolutionary computation (CEC 2004), Portland, pp 105–116

Trojanowski K (2007) B-cell algorithm as a parallel approach to optimization of moving peaks benchmark tasks. In: Proceedings of the 6th international conference on computer information systems and industrial management applications, Elk, Poland, pp 143–148

Wang H, Wang D, Yang S (2007) Triggered memory-based swarm optimisation in dynamic environments. In: Applications of evolutionary computing, LNCS, vol 4448. Springer, Berlin, pp 637–646

Weise T, Zapf M, Chiong R, Nebro A (2009) Why is optimization difficult?. In: Chiong R (eds) Nature-inspired algorithms for optimisation, studies in computational intelligence, vol 193. Springer, Berlin, pp 1–50

Yang S (2005) Memory-based immigrants for genetic algorithms in dynamic environments. In: Proceedings of the genetic and evolutionary computation conference (GECCO 2005), Washington, DC, pp 1115–1122

Zou X, Wang M, Zhou A, Mckay B (2004) Evolutionary optimization based on chaotic sequence in dynamic environments. In: Proceedings of the IEEE international conference on networking, sensing and control, Taipei, Taiwan, pp 1364–1369