“Holland's and Kauffman's work, together with Dawkins' simulations of evolution and Varela's models of autopoietic systems, provide essential inspiration for the new discipline of artificial life, This approach, initiated by Chris Langton (1989, 1992), tries to develop technological systems (computer programs and autonomous robots) that exhibit lifelike properties, such as reproduction, sexuality, swarming, and co-evolution.”

— Francis Heylighen

" Complex Adaptive Systems http://pespmc1.vub.ac.be/CAS.html" on: Principia Cybernetica Web, Nov. 12, 1996
Principia Cybernetica Web, 1999-2003

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Francis Heylighen 9

Belgian cyberneticist 1960

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“Artificial Life is concerned with tuning the behaviors of such low-level machines that the behavior that emerges at the global level is essentially the same as some behavior exhibited by a natural living system… Artificial Life is concerned with generating lifelike behavior.”

Christopher Langton (1949) American computer scientist

Source: Artificial Life (1989), p.4-5 as cited in: Luis M. Rocha (2012) " The logical mechanisms of life http://informatics.indiana.edu/rocha/i-bic/lec02.html" on indiana.edu, August 27, 2012

“Many of the core ideas of cybernetics have been assimilated by other disciplines, where they continue to influence scientific developments. Other important cybernetic principles seem to have been forgotten, though, only to be periodically rediscovered or reinvented in different domains. Some examples are the rebirth of neural networks, first invented by cyberneticists in the 1940's, in the late 1960's and again in the late 1980's; the rediscovery of the importance of autonomous interaction by robotics and AI in the 1990's; and the significance of positive feedback effects in complex systems, rediscovered by economists in the 1990's. Perhaps the most significant recent development is the growth of the complex adaptive systems movement, which, in the work of authors such as John Holland, Stuart Kauffman and Brian Arthur and the subfield of artificial life, has used the power of modern computers to simulate and thus experiment with and develop many of the ideas of cybernetics. It thus seems to have taken over the cybernetics banner in its mathematical modelling of complex systems across disciplinary boundaries, however, while largely ignoring the issues of goal-directedness and control.”

Francis Heylighen (1960) Belgian cyberneticist

Source: Cybernetics and Second-Order Cybernetics (2001), p.5 : About the state of the art of contemporary cybenetics

“Simulated evolution on a computer works but is no where near the gradual incremental process that is associated with Darwinian evolution. It's closer to dog breeding in terms of its computational complexity.”

Robert J. Marks II (1950) American electrical engineering researcher and intelligent design advocate

In the universe, [besides] space, matter and energy, there is information. [Information hasn't yet] been [well] defined nor studied.
Many times proponents of evolutionary computing … refuse to recognize the contribution of [the programmer's infusion of information] into the process.
Association with ID (intelligent design) in any way is detrimental to one's career. Everybody who works in ID should first have tenure before they come out of the closet.
My comments are as an expert in computational intelligence. I'm not a biologist. For me to talk about the details of biology is as stupid as a British biologist claiming expertise in religion. (A reference to Richard Dawkins.)
Engineers actually design things. This is why [many] engineers are interested in the area of intelligent design
"Well-Informed: Dr. Robert Marks and the Evolutionary Informatics Lab,", From an interview with Casey Luskin of the pro-intelligent design Discovery Institute, July 20, 2007, 2010-05-06 http://www.idthefuture.com/2007/07/wellinformed_dr_robert_marks_a.html,

“Artificial Life (``AL or ``Alife) is the name given to a new discipline that studies "natural" life by attempting to recreate biological phenomena from scratch within computers and other "artificial" media. Alife complements the traditional analytic approach of traditional biology with a synthetic approach in which, rather than studying biological phenomena by taking apart living organisms to see how they work, one attempts to put together systems that behave like living organisms.”

Christopher Langton (1949) American computer scientist

Christopher Langton in: Karl Gerbel, Peter Weibel, Katharina Gsöllpointner (1993) Genetische Kunst--künstliches Leben. p.25

“There are two fundamental ways of (re-)engineering information systems. The “formal driven” approach is based on the goal of developing and implementing a technical correct running system. The “content driven” approach is based on the goal of developing and implementing an organizational correct running system. By using reference models, content and technology can be combined in a new way”

August-Wilhelm Scheer (1941) German business theorist

Source: ARIS architecture and reference models for business process management (2000), p. 376.

“Advances in hardware and network technologies have paved the way for the development of increasingly pervasive software-based systems of systems that collaborate to provide essential services to society. Software in these systems is often required to (1) operate in distributed and embedded computing environments consisting of diverse devices (personal computers, specialized sensors and actuators), (2) communicate using a variety of interaction paradigms (e. g., SOAP messaging, media streaming), (3) dynamically adapt to changes in operating environments, and (4) behave in a dependable manner. Despite signiﬁcant advances in programming languages and supporting integrated development environments (IDEs), developing these complex software systems using current code-centric technologies requires herculean effort.”

Bernhard Rumpe (1967) German computer scientist

Source: Model-driven development of complex software: A research roadmap (2007), p. 37: Introduction

“If the system exhibits a structure which can be represented by a mathematical equivalent, called a mathematical model, and if the objective can be also so quantified, then some computational method may be evolved for choosing the best schedule of actions among alternatives. Such use of mathematical models is termed mathematical programming.”

George Dantzig (1914–2005) American mathematician

Source: Linear programming and extensions (1963), p. 2

“The growing complexity of software is the motivation behind work on industrializing software development. In particular, current research in the area of model driven engineering (MDE) is primarily concerned with reducing the gap between problem and software implementation domains through the use of technologies that support systematic transformation of problem-level abstractions to software implementations. The complexity of bridging the gap is tackled through the use of models that describe complex systems at multiple levels of abstraction and from a variety of perspectives, and through automated support for transforming and analyzing models. In the MDE vision of software development, models are the primary artifacts of development and developers rely on computer-based technologies to transform models to running systems.”

Bernhard Rumpe (1967) German computer scientist

Source: Model-driven development of complex software: A research roadmap (2007), p. 37

“With the growing interest in complex adaptive systems, artificial life, swarms and simulated societies, the concept of “collective intelligence” is coming more and more to the fore. The basic idea is that a group of individuals (e. g. people, insects, robots, or software agents) can be smart in a way that none of its members is. Complex, apparently intelligent behavior may emerge from the synergy created by simple interactions between individuals that follow simple rules.”

Francis Heylighen (1960) Belgian cyberneticist

Source: Collective Intelligence and its Implementation on the Web (1999), p.253

“In sum, the processes of evolution create initially comparatively simple dynamical systems on particular levels of organisation. The processes then lead to the progressive complexification of the existing systems and, ultimately, to the creation of simpler systems on the next higher organisational level, where complexification begins anew. Thus evolution moves from the simpler to the more complex, and from the lower to the higher level of organisation”

Ervin László (1932) Hungarian musician and philosopher

Laszlo (1991) The Age of Bifurcation: Understanding the Changing World. Philadelphia: Gordon & Breach. p. 112; As cited in: K.L. Dennis (2003, p. 69).

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