Similarly, by applying lateral-computing techniques to a problem, it can become much easier to arrive at a computationally inexpensive, easy to implement, efficient, innovative or unconventional solution.
The traditional or conventional approach to solving computing problems is to either build mathematical models or have an IF- THEN -ELSE structure.
For example, a brute-force search is used in many chess engines,[2] but this approach is computationally expensive and sometimes may arrive at poor solutions.
[citation needed] Lateral-computing sometimes arrives at a novel solution for particular computing problem by using the model of how living beings, such as how humans, ants, and honeybees, solve a problem; how pure crystals are formed by annealing, or evolution of living beings or quantum mechanics etc.
The lateral thinking technique proposes to escape from this patterning to arrive at better solutions through new ideas.
The random mutation works as a provocative information processing and provides a new avenue for generating better solutions for the computing problem.
Here is a brief description of some of the Lateral Computing techniques: Swarm intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) agents, interacting locally with their environment, cause coherent functional global patterns to emerge.
[clarification needed][5] SI provides a basis with which it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model.
[clarification needed][7] Agents are considered to be autonomous (independent, not-controllable), reactive (responding to events), pro-active (initiating actions of their own volition), and social (communicative).
As widely varied individual agents interact in the model, the simulation shows how their collective behaviors govern the performance of the entire system - for instance, the emergence of a successful product or an optimal schedule.
These simulations are powerful strategic tools for "what-if" scenario analysis: as managers change agent characteristics or "rules," the impact of the change can be easily seen in the model output By analogy, a computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities.
[8] The applications of grid computing are in: The autonomic nervous system governs our heart rate and body temperature, thus freeing our conscious brain from the burden of dealing with these and many other low-level, yet vital, functions.
There are numerous advantages of using optical devices for computing such as immunity to electromagnetic interference, large bandwidth, etc.
Since the DNA molecule is also a code, but is instead made up of a sequence of four bases that pair up in a predictable manner, many scientists have thought about the possibility of creating a molecular computer.
[clarification needed] These computers rely on the much faster reactions of DNA nucleotides binding with their complements, a brute force method that holds enormous potential for creating a new generation of computers that would be 100 billion times faster than today's fastest PC.
Example applications of DNA computing are in solution for the Hamiltonian path problem which is a known NP[clarification needed] complete one.
[clarification needed][12] Molecular algorithms have been reported to solve the cryptographic problem in a polynomial number of steps.
In applying these gates in succession, a quantum computer can perform a complicated unitary transformation to a set of qubits in some initial state.
Field-programmable gate arrays (FPGA) are making it possible to build truly reconfigurable computers.
[16] The Simulated annealing algorithm is designed by looking at how the pure crystals form from a heated gaseous state while the system is cooled slowly.
The slow and regular cooling of the metal allows the atoms to slide progressively their most stable ("minimal energy") positions.
By simulating the process of annealing inside a computer program, it is possible to find answers to difficult and very complex problems.
One of the main components of "Lateral-computing" is soft computing which approaches problems with human information processing model.
Here is a small set of applications that illustrates lateral computing: Above is a review of lateral-computing techniques.
Lateral-computing is based on the lateral-thinking approach and applies unconventional techniques to solve computing problems.
The lateral-computing successfully tackles a class of problems by exploiting tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost.
Lateral-computing techniques which use the human like information processing models have been classified as "Soft Computing" in literature.
Lateral-computing is valuable while solving numerous computing problems whose mathematical models are unavailable.
[citation needed] They provide a way of developing innovative solutions resulting in smart systems with Very High Machine IQ (VHMIQ).