Genetic operators
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Genetic Operations - use of operators to probabilistically transform a set of schemata into a child population

Summary
Plans change in complex adaptive systems (
This page introduces the complex adaptive system (CAS) theory frame.  The theory is positioned relative to the natural sciences.  It catalogs the laws and strategies which underpin the operation of systems that are based on the interaction of emergent agents. 
John Holland's framework for representing complexity is outlined.  Links to other key aspects of CAS theory discussed at the site are presented. 
CAS
) due to the action of genetic operations such as mutation, splitting and recombination.  The nature of the operations is described. 
Introduction
Holland identified a process the genetic algorithm that results in the iterative generation of a limited population of "fit" schemata from a current set and a current situation.  It's a simplified evolutionary algorithm

Holland's process "selects" schemata to be recombined into a subsequent generation.  Operators' probabilistically transform a selected pair of schemata.  Hence the process uses a sexual reproductive enforces the mixing of current germ-line DNA of a male and a female organism, with a recombination process, to ensure the generation of new schematic recipes and phenotypes in their shared offspring.  model.  The genetic pooling and emergent adaptability of progressive generations is a significant benefit to
This page introduces the complex adaptive system (CAS) theory frame.  The theory is positioned relative to the natural sciences.  It catalogs the laws and strategies which underpin the operation of systems that are based on the interaction of emergent agents. 
John Holland's framework for representing complexity is outlined.  Links to other key aspects of CAS theory discussed at the site are presented. 
the complex adaptive system
(CAS). 

Holland's original operators included: Inversion, Recombination, Mutation and Dominance. 

Mutation introduces new aspects into a CAS.   Useful mutations typically create small changes in the
Plans are interpreted and implemented by agents.  This page discusses the properties of agents in a complex adaptive system (CAS). 
It then presents examples of agents in different CAS.  The examples include a computer program where modeling and actions are performed by software agents.  These software agents are aggregates. 
The participation of agents in flows is introduced and some implications of this are outlined. 
agents
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
Darwinian evolution
Richard Dawkin's explores how nature has created implementations of designs, without any need for planning or design, through the accumulation of small advantageous changes. 
creates statistically unlikely agents through cumulative small changes
.  As such any single large change is likely to drive the agent away from its currently useful structure. 

In DNA (DNA), a polymer composed of a chain of deoxy ribose sugars with purine or pyrimidine side chains.  DNA naturally forms into helical pairs with the side chains stacked in the center of the helix.  It is a natural form of schematic string.  The purines and pyrimidines couple so that AT and GC pairs make up the stackable items.  A code of triplets of base pairs (enabling 64 separate items to be named) has evolved which now redundantly represents each of the 20 amino-acids that are deployed into proteins, along with triplets representing the termination sequence.  Chemical modifications and histone binding (chromatin) allow cells to represent state directly on the DNA schema.  To cope with inconsistencies in the cell wide state second messenger and evolved amplification strategies are used. 
based systems replacement mutations are substitutions of one base, a nucleotide base is the side chain purine (A or G) or pyrimidine (T or C).  A is a natural pair for T.  G pairs naturally with C.  These bases have multiple uses in cells including energy transfer, second messenger signalling as well as genetic data storage, transcription and translation.  Deacon argues that the multiple uses are significant to the emergence of evolution. 
pair for another.  With the requirement to transcribe is the process where DNA is converted into messenger m-RNA.  A complex of enzymes cooperates to bind to the DNA and generate the m-RNA copy.  There are a number of such transcription complexes which are based on RNA polymerase I, II or III. 
and translate is the process where messenger m-RNA is cross coded by Ribosomal agents and t-RNA into an amino-acid polymer. 
the DNA sequences into proteins, a relatively long chain (polymer) of peptides.  Shorter chains of peptides are termed polypeptides.   the encoding, the mapping of DNA base triplet sequences, such as AAA and AAT, to amino-acids (AAA maps to the amino-acid lysine for example) and transcription termination sequences (TGA maps to stop transcription for example) that has currently evolved. 
constrains these mutations to be reflected as:

An important adjunct of mutation is replication of a part of a schematic string.  This allows a mutation to act on a 'copy' of an important facet of the schematic structure.  Such mutations will not be catastrophic when they alter required facets of the agent. 

In cellular systems certain structural domains, evolution conserves many useful structures including DNA base sequence (content) addressable binding regions, protein active sites and signal structures which can then be reused through the mutation genetic operator. 
are preserved by
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
evolutionary selection
and leveraged as
This page discusses the physical foundations of complex adaptive systems (CAS).  A small set of rules is obeyed.  New [epi]phenomena then emerge.  Examples are discussed. 
phenomenological motifs
.  Recombination can leverage these domains and enable oligomerization which brings together binding and control domains.  The effect of recombination on selectable effects is extended by the presence of epistatic sequences. 

Prokaryotic, a single cell system exemplified by the bacteria.  Prokaryotes have their own DNA and infrastructure within a single enclosure.   bacteria can augment the recombination operator with plasmids and R-factors provide bacteria with a way to transfer parts of their DNA complement with one another.  The effect is to ensure that useful mutations can become rapidly distributed within a population of bacteria.   which enable the sharing of schematic material (DNA) between bacteria.  The mixing can then be augmented by recombination across the schematic strings. 

Recombination provides
Lou Gerstner describes the challenges he faced and the strategies he used to successfully restructure the computer company IBM. 
compartmentalized systems
with great
To benefit from shifts in the environment agents must be flexible.  Being sensitive to environmental signals agents who adjust strategic priorities can constrain their competitors. 
flexibility
, since additional states can potentially be supported. 

Using John Holland's theory of adaptation in complex systems Baldwin and Clark propose an evolutionary theory of design.  They show how this can limit the interdependencies that generate complexity within systems.  They do this through a focus on modularity. 
Baldwin & Clark
applied Holland's ideas to
This page discusses the strategy of modularity in a complex adaptive system (CAS).  The benefits, mechanism and its emergence are discussed. 
modularity
in design.  They concluded that the effect of modular design is to create new modular operators:
  • Splitting - of a system in to multiple modules.  Substituting - one module design for another.  Allowing improvement. 
  • Augmenting - adding a new module to a system.
  • Excluding - a module from the system.
  • Inverting to create new design rules.  Allowing redundant activities to be consolidated into single modules. 
  • Porting a module to another system.  Allowing systems to be linked by common modules.
Complex adaptive
Plans are interpreted and implemented by agents.  This page discusses the properties of agents in a complex adaptive system (CAS). 
It then presents examples of agents in different CAS.  The examples include a computer program where modeling and actions are performed by software agents.  These software agents are aggregates. 
The participation of agents in flows is introduced and some implications of this are outlined. 
agents
can generally use genetic operators expressed through reproduction as new
This page reviews the implications of reproduction initially generating a single child cell.  The mechanism and resulting strategic options are discussed. 
organisms
where modular components
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emerge


The creation of these modular systems hugely increases the
To benefit from shifts in the environment agents must be flexible.  Being sensitive to environmental signals agents who adjust strategic priorities can constrain their competitors. 
flexibility
and adaptability of the development process enabling the emergence of an eco-network. 

Holland's genetic algorithm enables an abstraction of the evolutionary process which can be applied directly by design to situations where the environment is fixed and corresponds to a series of situations that each provide
Agents use sensors to detect events in their environment.  This page reviews how these events become signals associated with beneficial responses in a complex adaptive system (CAS).  CAS signals emerge from the Darwinian information model.  Signals can indicate decision summaries and level of uncertainty. 
signals
and are associated with defined actions of an agent.  The genetic algorithms schemata become simple arrays of actions indexed by signalled situation.  In such a situation the operation of an action can also be associated with a value of a fitness function. 

Evolutionary algorithms
To generate truly
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent
systems the assumption that the states, actions, and signals, can be pre-assigned must be abandoned.  New environmental niches, information, constraints, structures and dynamics must emerge.  Typically the agent must
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
model
its previous successes, current signals, environment as well as other agents' likely actions to represent its situation.  In the struggle to survive a current agent may utilize schematically recombined mechanisms during its operation which allow it to enter a new environmental niche (implying additional emergent states).  The
Plans emerge in complex adaptive systems (CAS) to provide the instructions that agents use to perform actions.  The component architecture and structure of the plans is reviewed. 
schematic strings
are consequently more feature rich than the simple genetic algorithm's situation addressed action arrays. 
































































































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This page looks at schematic structures and their uses.  It discusses a number of examples:
  • Schematic ideas are recombined in creativity. 
  • Similarly designers take ideas and rules about materials and components and combine them. 
  • Schematic Recipes help to standardize operations. 
  • Modular components are combined into strategies for use in business plans and business models. 

As a working example it presents part of the contents and schematic details from the Adaptive Web Framework (AWF)'s operational plan. 

Finally it includes a section presenting our formal representation of schematic goals. 
Each goal has a series of associated complex adaptive system (CAS) strategy strings. 
These goals plus strings are detailed for various chess and business examples. 
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  • A business can gain focus from targeting key customers,
  • Business planning activities performed by the whole organization can build awareness, empowerment and coherence. 
  • A program approach can ensure strategic alignment. 
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