In   this   talk   I   will   overview   ten   years of   research   in   the  application  of  evolutionary  computation  ideas  in  the  natural   sciences.    The  talk  will  take  us  on  a  tour  that  will  cover  problems   in   nanoscience,   e.g.   controlling   self-­‐organizing   systems,   optimizing   scanning   probe   microscopy,   etc.,   problems   arising   in   bioinformatics,   such   as   predicting   protein   structures   and   their   features,   to   challenges   emerging   in   systems   and   synthetic   biology.     Although   the   algorithmic   solutions   involved   in   these   problems  are  different  from  each  other,  at  their  core,  they  retain   Darwin’s   wonderful   insights.     I   will   conclude   the   talk   by   giving   a   personal   view   on   why   EC   has   been   so   successful   and   where,   in   my  mind,  the  future  lies.

1. Darwin’s Magic: Evolutionary Computation in Nanoscience, Bioinformatics, Systems & Synthetic Biology Prof. Natalio Krasnogor Automated Scheduling, Optimisation and Planning Research Group School of Computer Science, University of Nottingham www.cs.nott.ac.uk/~nxk twitter.com/NKrasnogor Page 1 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

2. Outline Darwin’s Magic and Algorithmic Beauty Evolutionary Computation in the Natural Sciences Self-Assembly and Scanning Probe Microscopy Optimisation Structural Bioinformatics Systems Biology & Synthetic Biology On Invariants, Decorations and the Future Conclusions Page 2 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

3. Outline Darwin’s Magic and Algorithmic Beauty Evolutionary Computation in the Natural Sciences Self-Assembly and Scanning Probe Microscopy Optimisation Structural Bioinformatics Systems Biology & Synthetic Biology On Invariants, Decorations and the Future Conclusions Page 3 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

4. Darwin’s Magic Page 4 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011 Thank you Youtube

5. Algorithmic Beauty Inheritable Instructions Set Limited Resources Imperfect Replication A Powerful Secondary Effect: Selection An awe inspiring product: Evolution by Natural Selection Page 5 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

6. Outline Darwin’s Magic and Algorithmic Beauty Evolutionary Computation in the Natural Sciences Self-Assembly and Scanning Probe Microscopy Optimisation Structural Bioinformatics Systems Biology & Synthetic Biology On Invariants, Decorations and the Future Conclusions Page 6 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

7. Evolutionary Computation in the Natural Sciences Programmable algorithmic entry to the vast world of nanoscale physical, chemical & biological systems and processes Algorithmic and Artificial Living Matter (ALMA) A Research Vision How (?) do you gain algorithmic entry into Embedded behavior Information & Algorithms Complexity Robustness Tradeoffs Computer Science How does “The Logistics of Small Things” look like? Page 7 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

8. The Spatial Scales Involved Page 8 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

9. ALMA & The Logistics of Small Things How do you program complex nano/micro scale process : through billions of tiny & simple distributed programs/processors? when there is no clear distinction between hardware and software? when the wetware is not simply a stochastic program: when wetware is poorly characterised and is likely to evolve, etc. function f1(p1,p2,p3,p4) { if (p1<p2) and (rand<0.5) print p3 else print p4 } function f1(p1,p2,p3,p4) { if (p1<p2) RND print p3 RND else RND print p4 RND } function f1(p1,p2,p3,p4) { if (p1<p2) RND print p3 RND else RND print p4 RND } function f1(p1,p2,p3,p4) { if (p1<p2) RND incr p3 RND else RND decr p4 RND } Page 9 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

10. Outline Darwin’s Magic and Algorithmic Beauty Evolutionary Computation in the Natural Sciences Self-Assembly and Scanning Probe Microscopy Optimisation Structural Bioinformatics Systems Biology Synthetic Biology On Invariants, Decorations and the Future Conclusions Page 10 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

11. The Spatial Scales Involved Page 11 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

12. Molecular Tiles & Programmable Self-Assembly Algorithmic Self-Assembly of DNA Sierpinski Triangles. P.W.K. Rothemund, N. Papadakis, E. Winfree. PLoS Biology 2:12 (2004) Page 12 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

14. Tiles with deterministic assembly (Model 1) Tiles with probabilistic assembly (Model 2) Page 14 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

15. Evolutionary Design Approach Variable length individuals (Genotype) Genotype -Phenotype Mapping Randomly created Wang tiles One-point crossover Phenotype Bitwise mutation Phenotype – Fitness Mapping Minkowski functionals (A, P, X) A = 12 P = 24 X = 0 A = 100 P = 40 X = 1 Population size = 100, Individuals length = [1,10], Generations = 300, Pcrossover= 0.7, PMutation= 0.1/0.05/0.01 Vs Page 15 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

16. Probabilistic Assembly + No Rotation Probabilistic Assembly + Rotation Deterministic Assembly + Rotation Deterministic Assembly + No Rotation Page 16 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

17. How Does Self-Assembly Gets Programmed? Two-tile self-assembly Three-tile self-assembly Four-tile self-assembly Five-tile self-assembly We calculated the equivalence classes of binding pockets defined by “bp1 R bp2 iif NAFE(bp1)=NAFE(bp2)” for the best tile set. We observed thatequivalence classes with NAFE smaller than T are highly likely to participate in the self-assembly process as these are more populous. More “assembable” binding pockets = Generalised Secondary Structures Page 17 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

19. Motion along a line of tiles

20. Motion without interactionDiffusion across terraces on the substrate Intramolecule strength: energy between two no-functionalised porphyrins Molecule-substrate strength: energy of a porphyrin to the substrate Rotational strength: molecule-substrate strength for spinning Page 18 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

21. How Do You Image and Manipulate at This Scale? D. M. Eigler & E. K. Schweizer, Nature 344, 524 - 526 (1990) C60 Y. Sugimoto et al., Nature letters 446, 64 (2007). Hlaet al. Phys. Rev. Lett. 85, 2777–2780 (2000) D.L. Keeling et al. Phys. Rev. Lett 94, 146104 (2005) Page 19 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

22. Scanning tip Z A X Y Sample surface Axis under direct (piezo) control Even 3 Variable Problems are Difficult: Optimising a Scanning Probe Microscopy it ∝ exp(−2kd) i G http://www2.fz-juelich.de/ibn/index.php?index=1021 V The tunnel current it is highly dependant on the tip-sample distance, d. This current can be maintained with a feedback loop, G, that actively controls the tip-sample distance. Page 20 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

23. Understanding the image J. H. A. Hagelaaret al. PRB 78, 161405R 2008 L.Gross et al. Science 325 1110 (2009) Page 21 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

24. (Un)Stable and (Un)defined Tip States Imaging problems, spontaneous tip changes Page 22 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

25. Two Stage Automation Process Automated probe microscopy via evolutionary optimisation at the atomic scale. R. Woolley, J. Sterling, A. Radocea, N. Krasnogor and P. Moriarty. Applied Physical Letters (to appear) Cellular GA with Smart Initialisation In-situ Ex-situ Voltage pulsing (deliberate crash) Fine tuning (changing scan parameters) Page 23 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

26. Stage 1: Smart Initialisation (coarsely) Conditions the Probe Streaky Image. Executing cleaning pulse A deterministic approach Cloudy Image. Executing cleaning pulse Flat Surface. Zooming in to 50nm Flat Surface. Zooming in to 20nm Constant Atomic resolution. Zooming in to 4nm Poor Atomic resolution. Rescanning Consistent fair atomic resolution. Stage 1 complete. Time elapsed: 1010.1902 (~17mins) Page 24 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

27. G V i G G G V V V i i i Stage 2: Fine adjustment with CGA Starting image Cellular GA G V i Machine Optimised Page 25 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

30. How Does it Compares to an Expert Operator? Page 27 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

31. Outline Darwin’s Magic and Algorithmic Beauty Evolutionary Computation in the Natural Sciences Self-Assembly and Scanning Probe Microscopy Optimisation Structural Bioinformatics Systems Biology & Synthetic Biology On Invariants, Decorations and the Future Conclusions Page 28 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

32. The Spatial Scales Involved Page 29 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

33. Protein Folding & Structure Prediction Anfinsen’s thermodynamic hypothesis [Anfinsen 1973, Dill and Chan 1997] Primary Sequence 3D Structure Protein Structure Prediction (PSP) aims to predict the 3D structure of a protein based on its primary sequence (perhaps disregarding the folding process) Page 30 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

34. Defining and Predicting Useful Features M. Stout, J. Bacardit, J. Hirst & N. Krasnogor, Bioinformatics 2008 24(7):916-923. Contact M. Stout, J. Bacardit, J.D. Hirst, R.E Smith, and N. Krasnogor. Prediction of topological contacts in proteins using learning classifier systems. Journal Soft Computing - A Fusion of Foundations, Methodologies and Applications, 13(3):245-258, 2008. Page 31 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

36. Solvent Accessibility

37. Recursive Convex Hull

38. Coordination NumberIntegration of all these predictions plus other sources of information Final CM prediction (using BioHEL) Using BioHEL Page 32 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

39. The BioHEL GBML System BIOinformatics-oriented Hiearchical Evolutionary Learning – BioHEL(Bacardit & Krasnogor, 2009) BioHEL is a rule-based evolutionary learning system that employs the Iterative Rule Learning (IRL) paradigm First used in EC in Venturini’s SIA system (Venturini, 1993) Widely used for both Fuzzy and non-fuzzy evolutionary learning J. Bacardit, M. Stout, J.D. Hirst, K. Sastry, X. Llora, and N. Krasnogor. Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. Proceedings of the 2007 Genetic and Evolutionary Computation Conference, ACM Press, 2007. J. Bacardit, M. Stout, J.D. Hirst, A. Valencia, R.E. Smith, and N. Krasnogor. Automated alphabet reduction for protein datasets. BMC Bioinformatics, 10(6), 2009. Bronze Medal in the THE 2007 “HUMIES” AWARDS FOR HUMAN-COMPETITIVE RESULTS PRODUCED BY GENETIC AND EVOLUTIONARY COMPUTATION. Page 33 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

41. We predict them from the closest neighbours in the chainRi SSi Ri-1 SSi-1 Ri+2 SSi+2 Ri-2 SSi-2 Ri+3 SSi+3 Ri+4 SSi+4 Ri-3 SSi-3 Ri-4 SSi-4 Ri-5 SSi-5 Ri+5 SSi+5 Ri+1 SSi+1 Ri-1 Ri Ri+1 SSi Ri Ri+1 Ri+2 SSi+1 Ri+1 Ri+2 Ri+3  SSi+2 Page 34 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

43. Moreover, all proteins with more than 350 amino acids were discarded

44. Still, the resulting training set contained more than 15.2 million instances and 631 attributes

45. Less than 2% of those are actual contacts

46. 36GB of disk spacePage 35 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

48. BioHEL is run 25 times for each sample

49. Prediction is done by a consensus of 1250 rule sets

50. Confidence of prediction is computed based on the votes distribution in the ensemble.

51. Whole training process takes about 289 CPU days (~5.5h/rule set)x50 Samples x25 Rule sets Consensus Predictions Page 36 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

53. parallel prediction and experimental verification

55. 140 server groupsPage 37 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

56. Contact Map prediction in CASP 7 Accuracy for groups that predicted a common subset of targets Ezkudia et al. Proteins 2009; 77(Suppl 9):196-209 Page 38 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

57. Xd results Contact Map prediction in CASP 7 Ezkudia et al. Proteins 2009; 77(Suppl 9):196-209 Page 39 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

59. 67% accuracyEzkudia et al. Proteins 2009; 77(Suppl 9):196-209 Page 40 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

61. Set of 3262 proteins for training all the 1D predictors

62. A subset of 2413 proteins used for CM prediction

63. All proteins with less than 250AA

64. A randomly selected 20% for larger chains

65. 50 Samples of ~660000 instances were generated

66. The representation remained unchanged

67. 25K CPU hours were employed just to train the CM ensemblePage 41 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

68. In terms of performance These two groups derived contact predictions from 3D models Page 42 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

71. structure optimisation

72. Folding@home 8.5 peta FLOPS

73. 10 000 CPU days for 10μs of folding[Dill and Chan 1997] P. Widera, J.M. Garibaldi, J., and N. Krasnogor,. Evolutionary design of the energy function for protein structure prediction, Proceedings of the IEEE Congress on Evolutionary Computation 2009. P. Widera, J. Garibaldi, and N. Krasnogor. GP challenge: evolving the energy function for protein structure prediction. Journal of Genetic Programming and Evolvable Machines, 11:61-88, 1 2010. Gold Medal in the THE 2010 “HUMIES” AWARDS FOR HUMAN-COMPETITIVE RESULTS PRODUCED BY GENETIC AND EVOLUTIONARY COMPUTATION Page 43 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

75. distance reflects similarityPage 44 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

77. I-TASSER [Wu et al. 2007]

78. Robetta [Rohl et al. 2004]Page 45 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

80. I-TASSER [Wu et al. 2007]

81. Robetta[Rohl et al. 2004]Page 46 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

83. functions:add sub mul divsin cos exp log

84. random ephemerals in range [0,1]Zhang et al. 2003 Page 47 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

86. 5 different ranking distance measures

87. 20 different configurations of GP parameters

88. total of 150 CPU daysPage 48 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

89. Outline Darwin’s Magic and Algorithmic Beauty Evolutionary Computation in the Natural Sciences Self-Assembly and Scanning Probe Microscopy Optimisation Structural Bioinformatics Systems Biology & Synthetic Biology On Invariants, Decorations and the Future Conclusions Page 49 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

90. The Spatial Scales Involved Page 50 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

91. The Cell as an Information Processing Device LeDuc et al. Towards an in vivo biologically inspired nanofactory. Nature (2007) Page 51 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

92. Transcription Networks Environment Signal2 Signal5 Signal1 Signal3 Signal4 Signaln ... Transcription Factors Genome Gene1 Gene2 Gene3 Genek Page 52 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

93. Network Motifs: Evolution’s Preferred Circuits Biological networks are complex and vast Moreover, these patterns are organised in non-trivial/non-random hierarchies “Patterns that occur in the real network significantly more often than in randomized networks are called network motifs” Shai S. Shen-Orr et al., Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics 31, 64 - 68 (2002) RaduDobrin et al., Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network. BMC Bioinformatics. 2004; 5: 10. The C1-FFL is a ‘sign-sensitive delay’ element and a persistence detector. Each network motif carries out a specific information-processing function The I1-FFL is a pulse generator and response accelerator Page 53 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

94. Evolvable Executable Biology Page 54 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

95. Nested EA for Model Synthesis F. Romero-Campero, H.Cao, M. Camara, and N. Krasnogor. Structure and parameter estimation for cell systems biology models. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2008), pages 331-338. ACM Publisher, 2008. Best Paper Award H. Cao, F.J. Romero-Campero, S. Heeb, M. Camara, and N. Krasnogor. Evolving cell models for systems and synthetic biology. Systems and Synthetic Biology , 2009 Page 55 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

97. Different time series have very different profiles, e.g., response time or maxima occur at different times/places

98. Transient states (sometimes) as important as steady states

99. RMSE will mislead search

100. Sometimes the time series is qualitative or microarray dataH. Cao, F.J. Romero-Campero, S. Heeb, M. Camara, and N. Krasnogor. Evolving cell models for systems and synthetic biology. Systems and Synthetic Biology , 2009 Page 56 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

101. Problem Specification Page 57 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

102. Target Page 58 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

103. A Signal Translatorfor Pattern Formation FP2 FP1 act1 act2 rep1 rep2 rep3 rep4 I2 I1 Pact1 Prep3 Prep2 Pact1 Prep1 Pact2 Prep2 Prep4 Prep1 Pact2 Page 59 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

104. Uniform Spatial Distribution of Signal Translators for Pattern Formation pBR322 pACYC184 E. coli DH5α ∆sdiA/∆lacI (2∆) Page 60 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

105. Pattern Formation in synthetic bacterial colonies Page 61 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

106. pAYCP (1-3) pBR322 (4-6) Starting OD=10 2∆ DH5α Magnification: 100X Page 62 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

107. pUC6S (1-6) Starting OD= 10 Magnification: 40X 2∆ DH5α Page 63 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

109. Algorithms are Tiny Factoring: Let n be the number to be factored. 1. Let Δ be a negative integer with Δ = -dn where d is a multiplier and Δ is the negative discriminant of some quadratic form. 2. Take the t first primes , for some . 3. Let fq be a random prime form of GΔ with . 4. Find a generating set X of GΔ 5. Collect a sequence of relations between set X and {fq : q ∈ PΔ} satisfying: 6. Construct an ambiguous form (a, b, c) which is an element f ∈ GΔ of order dividing 2 to obtain a coprime factorization of the largest odd divisor of Δ in which Δ = -4a.c or a(a - 4c) or (b - 2a).(b + 2a) 7. If the ambiguous form provides a factorization of n then stop, otherwise find another ambiguous form until the factorization of n is found. In order to prevent that useless ambiguous forms are generated, build up the 2-Sylow group S2(Δ) of G(Δ). Calculating Pi Page 65 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

111. They are not short pieces of code, but large systemsPage 66 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

112. What are Evolutionary Algorithms? Research Paradigms for Problem Solving T.S. Kuhn. The Structure of Scientific Revolutions, 1962. Design Patterns and Pattern Languages C. Alexander, S. Ishikawa, M. Silverstein, M. Jacobson, I. Fiksdahl-King, S. Angel, S.: A Pattern Language - Towns, Buildings, Construction. Oxford University Press (1977) N. Krasnogor and J.E. Smith.IEEE Transactions on Evolutionary Computation, 9(5):474- 488, 2005. Page 67 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

113. Invariants and Decorations A Compact “Memetic” Algorithm by Merz (2003) Page 68 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

114. Invariants and Decorations A “Memetic” Particles Swarm Optimisation by Petalas et al (2007) Page 69 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

115. Invariants and Decorations A “Memetic” Artificial Immune System by Yanga et al (2008) Page 70 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

116. Invariants and Decorations A “Memetic” Learning Classifier System by Bacardit & Krasnogor (2009) Page 71 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

117. Invariants and Decorations Many others based on Ant Colony Optimisation, NN, Tabu Search, SA, DE, etc. Key Invariants: Global search mode Local search mode Many Decorations, e.g.: Crossover/Mutations (EAs based MAs) Pheromones updates (ACO based MAs) Clonal selection/Hypermutations (AIS based MAs) etc Page 72 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

118. A Pattern Language for Memetic AlgorithmsMemetic Algorithms by N. Krasnogor. Handbook of Natural Computation (chapter) in Natural Computing. Springer Berlin / Heidelberg, 2009. www.cs.nott.ac.uk/~nxk/publications.html Page 73 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

119. solving 1 problem – single instances Solving multiple unrelated problem – several classes instances Programming Programming solving 1 problem – several instances (self) adaptive Programming Solving a few problem – several classes instances (self) adaptive Self-generating Programming (self) adaptive Self-generating Self-Engineering Reuse Reuse Feedback Reuse Feedback A General Trend: moving away from close-loop optimisation towards open-ended and embodied optimisation Effort (e.g. Time, $$$, etc) Effort (e.g. Time, $$$, etc) Effort (e.g. Time, $$$, etc) Effort (e.g. Time, $$$, etc) Page 74 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

120. The Future of EAsSoftware Nurseries Fundamental Change of Temporal Scales Rethink Software will be “seeded” and grown, very much like a plant or animal (including humans) Software will start in an “embryonic” state and develop when situated on a production environment What would a software “incubation” machine look like? What would a software “nursery” look like? Page 75 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

121. DNA/RNA Cells Individual Organs Tissue Specialised Function Potential To Develop into multiple different types of cells Ultimate Solver Commitment Page 76 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

122. Production Environment Input SC SC SC SC SC SC Software Cell TSP Organ Euclidean TSP Organ GraphicalTSP Organ TSP Solver Software Organism Pluripotential Solver “DNA” Page 77 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

123. TSP Solver Software Organism Protein Structure Prediction Solver Software Organism Vehicle Routing Solver Software Organism Graph Isomorphism Solver Software Organism SAT Solver Software Organism Bin Packing Solver Software Organism Graph Coloring Solver Software Organism Network Interdiction Solver Software Organism Quadratic Assignment Solver Software Organism An Ecosystem of solvers Page 78 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

124. As, e.g., Biologists & Physicists have done through an ubiquitous, worldwide spanning informatics infrastructure, we should be focusing on building an online worldwide computational problem solving infrastructure Page 79 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

126. Conclusions New types of executable structures In Nanotechnology DNA tiles, DNA origami, etc Non DNA based tiles Some have very definite programmable features Others require the program to be “distributed” and exploit noise and randomness In Synthetic Biology How to orchestrate activities at multiple temporal-spatial-energetic scales? How to cope with noise in the background that execures a program and in the program itself?! How to cope for programs that will evolve? Page 81 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

127. Conclusions New types of benchmarks Structural Biology (PSP and GP4PSP) Many of these problems can be modelled both as regression or classification problems Low/high number of classes Balanced/unbalanced classes Adjustable number of attributes Ideal benchmarks !! Scanning Probe Microscopy: Even a few dimensions are hard “Chameleons” as it is sampled http://www.infobiotic.net/ Page 82 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

129. Requires strong links with data mining, ALIFE and, of course, AI (beyond existing trends in constraint satisfaction), search based software engineering (beyond current trends on testing/debugging)

130. Requires on-line, computer friendly ontologies of code (e.g the pattern language in the left), self-describing source code, protocols for autonomic code reuse, etcPage 83 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

131. Conclusions Learn From Physics, Chemistry & Biology The Invariants & Patterns,the Decorations are superfluous Evolution Self-Assembly & Self-Organisation Developmental systems Depend on a core genome coding for essential functionality Epigenomicscanalises development Hierarchical control systems that modify programs including susceptibility to horizontal gene (program libraries) transfer Infrastructure Missing Components Missing Components Page 84 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

133. A.E. Smith

134. I. Parmee

135. G. Kendall

137. C. Alexander

138. P. Moriarty, P. Beton

139. N. Chapness, R. Wooley

140. M. Holdsworth, G. Basel

141. Colleagues at ASAPJ. Chaplin J. Blakes E. Glaab M. Franco Page 85 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011

142. Page 86 of 86 IEEE Congress on Evolutionary Computation - New Orleans, USA, 2011