Evolutionary Computation

(Computação Evolucionária)



last update: 10/09/2019 09:15

General issues:

  1. Bäck, T., Hammel, U., Schwefel, H.P. Evolutionary computation: comments on the history and current state. IEEE Transactions on Evolutionary Computation, v. 1, n. 1, p. 3-17, 1997.
  2. Tanomaru, J. Motivação, fundamentos e aplicações de algoritmos genéticos. In: II Congresso Brasileiro de Redes Neurais, Anais..., Curitiba, 20/10-01/11/1995, [s.p.]. obs: 17 Mbytes !
  3. Silva, A.P.A. Tutorial genetic algorithms. Learning and Nonlinear Models, v. 1, n. 1, p. 45-60, 2002.
  4. Lopes, H.S. Algoritmos genéticos em projetos de engenharia: aplicações e perspectivas futuras. In: IV Simpósio Brasileiro de Automação Inteligente, Anais..., São Paulo, 08-10/09/1999, p. 64-74, 1999.
  5. Hoffmeister, F., Bäck, T. Genetic algorithms and evolution strategies: similarities and differences. In: Proceedings of 1st Workshop Parallel Problem Solving from Nature - PPSN, Dortmund, Germany, p. 455-469, 1990.
  6. Krasnogor N. Memetic algorithms. Tutorial at 7th International Conference on Parallel Problem Solving from Nature (PPSN VII), Granada, Spain, sept. 2002.
  7. Bäck, T., Hoffmeister, F. Extended selection mechanisms in genetic algorithms. In: Proceedings of IV International Conference on Genetic Algorithms - ICGA, San Diego, USA, p. 92-99, 1991.
  8. Beasley, D., Bull, D.R., Martin, R.R., An Overview of Genetic Algortihms: Part 2, Research Topics. University Computing, v. 15, n. 4, p. 170-181, 1993.
  9. Cantú-Paz, E., A survey of parallel genetic algorithms. IlliGAL Technical Report 97003, 1997.
  10. Dolin, B., Merello, J.J. Resource review: a web-based tour of genetic programming. Genetic Programming and Evolvable Machines, v. 3, p. 311-313, 2002.
  11. Dorigo, M., Di Caro, G., Gambardella, L.M. Ant algorithms for discrete optimization. Arfificial Life, v. 5, n. 3, p. 137-172, 1999.
  12. Dorigo, M., Maniezzo, V., Colorni, A. The ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics -  Part B, v. 26, n. 1, p. 29-41, 1996.
  13. Castro, L.N., von Zuben, F.J. Artificial imunne systems: part I - basic theory and applications. Technical Report TR-DCA 01/99,  Universidade Estadual de Campinas, 1999, 49p.
  14. Castro, L.N., von Zuben, F.J. Artificial imunne systems: part II - a survey of applications. Technical Report TR-DCA 02/00,  Universidade Estadual de Campinas, 2000, 65p.
  15. Eberhart, R.C., Shi, Y. Particle swarm optimization: developments, applications and resources. Proceedings of the  Congress on Evolutionary Computation(CEC2001), Seul, Korea, v.1, p. 81-86, 2001.
  16. Eberhart, R. C. and Shi, Y. Comparison between genetic algorithms and particle swarm optimization, Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming, San Diego, CA., 1998.

Scheduling for 2019/1 (tentative!):




Lecture notes / exercises

Further reading


march, 13th

Introduction to Evolutionary Computation, history and paradigms. Overview of applications of CE metaheuristics in engineering, computer science and other areas. Search and optimization methods, multiobjective optimization. Genetic Algorithms:  Simple Genetic Algorithm (SGA), DeJong functions.

class1a class1b papers 1, 2, 3, 4


march, 20th

Types Terminology, formalization and operation of GAs. Criteria for terminating GAs. Encoding principles. Genetic operators  for binary, integer and real representation. Special genetic operators for combinatorial problems. Objective and fitness functions, constraints handling and penalties. Selection methods, elitism, generation gap. Selective pressure control methods, convergence and epistasia. Niches and speciation, crowding factor and other control methods.


papers 1 (p.4), 5, 7, 8   


march, 27th

Problem modelling and exercises. Training with GALLOPS software or other

class3 Inspyred_Intro

exercise #1: statement, solution; exercise #2: statement, .solution; exercise #3: statement, spreadsheet



april, 03rd

Hybrid and parallel Genetic Algorithms. Interaction between evolution and learning, Baldwin effect. Memetic algorithms. Parameter tunning and self-adjustment.


Genetic Programming: introduction, representation limitations of GA, program induction mechanism, tree-based representation of programs. Terminals and functions sets.



papers 6, 9, 10


april, 10th

Genetic Programming: genetic operators and control parameters. Classical applications of GP: symbolic regression and artificial ant. Designing the evolution of emergent behaviors. Evolution of classification with GP.

class6a class6b




april, 17th

Evolution of strategies.  Training with Lil-GP software or other. Extensions of GP: Gene Expression Programming.  

class7a class7b

exercise #4: statement, spreadsheet



april, 24th

Swarm Intelligence (1): Particle Swarm Optimization. Training & exercises

swarm-1 papers 11, 12, 15, 16


may, 01st NO CLASSES: LABOR DAY    


may, 08th

Swarm Intelligence (2): Ant Colony Optimization. Artificial Bee Colony, Gray Wolf Optimization. Training & exercises



may, 15th




may, 22th Differential Evolution. Other evolutionary computation paradigms. class8a  class8b   


may, 29th PROJECT PROPOSAL DUE: Short presentation and discussion of proposals for the final project, including: objective, methods, design of experiments.    
  july, 01st PROJECT REPORT DUE: Full report "paper-like" along with codes and data    





Date due



Upload link


Knapsack problem        


Vehicle transportation problem april, 17th   spreadsheet  


Time-series prediction may, 8th   data  


Capacitated Vehicle Routing Problem may, 22th   data  


  #1 #2 #3 #4 #5  project grade
  AG PG ACO/PSO        
Brenda Cinthya Solari Berno A A A     9,6 A
Leonardo Schneider A B X     6,5 B
Lucas Augusto Albini A X X     9,6 B
Luis Henrique de Oliveira Alves X X X     X E
Rodrigo Tchalski da Silva B X X     9,3 B
Rosanete Grassiani dos Santos X X X     X E


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